@misc{beck2024theoretical,
archivePrefix = {arXiv},
arxivId = {cs.LG/2402.13454},
author = {Beck, Nathan and Pham, Truong and Iyer, Rishabh},
eprint = {2402.13454},
primaryClass = {cs.LG},
title = {{Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection}},
year = {2024}
}
@article{Zhao,
archivePrefix = {arXiv},
arxivId = {arXiv:2212.06278v1},
author = {Zhao, Yidong and Yang, Changchun and Schweidtmann, Artur and Tao, Qian},
eprint = {arXiv:2212.06278v1},
file = {:P\:/cloud_data/Projects/Paper/2212.06278.pdf:pdf},
keywords = {nnu-net,stochastic gradient descent,uncertainty estimation,variational inference},
pages = {1--11},
title = {{Efficient Bayesian Uncertainty Estimation for nnU-Net}}
}
@inproceedings{MB23a,
annote = {Best Paper Award},
author = {Mittal, S and Niemeijer, J and Sch{\"{a}}fer, J and Brox, T},
booktitle = {German Conference on Pattern Recognition (GCPR)},
title = {{Best Practices in Active Learning for Semantic Segmentation}},
url = {http://lmb.informatik.uni-freiburg.de/Publications/2023/MB23a},
year = {2023}
}
@article{Iyer2021,
abstract = {Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property (also called second-order supermodular functions). We study specific instantiations of the submodular information measures, and see that they all have mathematically intuitive and practically useful expressions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy preserving summarization — and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning.},
archivePrefix = {arXiv},
arxivId = {2006.15412},
author = {Iyer, Rishabh and Khargonkar, Ninad and Bilmes, Jeff and Asnani, Himanshu},
eprint = {2006.15412},
file = {:P\:/cloud_data/Projects/Paper/2006.15412.pdf:pdf},
issn = {26403498},
journal = {Proceedings of Machine Learning Research},
keywords = {Combinatorial Information Measures,Information Theory,Submodular Optimization},
pages = {722--754},
title = {{Submodular Combinatorial Information Measures with Applications in Machine Learning}},
volume = {132},
year = {2021}
}
@article{Kaushal2020,
abstract = {We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks. While past work generally treats these problems differently ({\em e.g.}, different models are often used for generic and query-focused summarization), the submodular information measures allow us to study each of these problems via a unified approach. We first show that several previous query-focused and update summarization techniques have, unknowingly, used various instantiations of the aforesaid submodular information measures, providing evidence for the benefit and naturalness of these models. We then carefully study and demonstrate the modelling capabilities of the proposed functions in different settings and empirically verify our findings on both a synthetic dataset and an existing real-world image collection dataset (that has been extended by adding concept annotations to each image making it suitable for this task) and will be publicly released. We employ a max-margin framework to learn a mixture model built using the proposed instantiations of submodular information measures and demonstrate the effectiveness of our approach. While our experiments are in the context of image summarization, our framework is generic and can be easily extended to other summarization settings (e.g., videos or documents).},
archivePrefix = {arXiv},
arxivId = {2010.05631},
author = {Kaushal, Vishal and Kothawade, Suraj and Ramakrishnan, Ganesh and Bilmes, Jeff and Asnani, Himanshu and Iyer, Rishabh},
eprint = {2010.05631},
file = {:P\:/cloud_data/Projects/Paper/2010.05631.pdf:pdf},
pages = {1--35},
title = {{A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures}},
url = {http://arxiv.org/abs/2010.05631},
year = {2020}
}
@article{Gupta2020,
abstract = {In the submodular cover problem, we are given a monotone submodular function f : 2N → R+, and we want to pick the min-cost set S such that f(S) = f(N). This captures the set cover problem when f is a coverage function. Motivated by problems in network monitoring and resource allocation, we consider the submodular cover problem in an online setting. As a concrete example, suppose at each time t, a nonnegative monotone submodular function gt is given to us. We define f(t) = Ps≤t gs as the sum of all functions seen so far. We need to maintain a submodular cover of these submodular functions f(1), f(2), . . . f(T) in an online fashion; i.e., we cannot revoke previous choices. Formally, at each time t we produce a set St ⊆ N such that f(t)(St) = f(t)(N)-i.e., this set St is a cover-such that St−1 ⊆ St, so previously decisions to pick elements cannot be revoked. (We actually allow more general sequences {f(t)} of submodular functions, but this sum-of-simpler-submodular-functions case is useful for concreteness.) We give polylogarithmic competitive algorithms for this online submodular cover problem. The competitive ratio on an input sequence of length T is O(ln n ln(T {\textperiodcentered} fmax/fmin)), where fmax and fmin are the largest and smallest marginals for functions f(t), and |N| = n. For the special case of online set cover, our competitive ratio matches that of Alon et al. [AAA+09], which are best possible for polynomial-time online algorithms unless NP ⊆ BPP [Kor04]. Since existing offline algorithms for submodular cover are based on greedy approaches which seem difficult to implement online, the technical challenge is to (approximately) solve the exponential-sized linear programming relaxation for submodular cover, and to round it, both in the online setting. Moreover, to get our competitiveness bounds, we define a (seemingly new) generalization of mutual information to general submodular functions, which we call mutual coverage; we hope this will be useful in other contexts.},
author = {Gupta, Anupam and Levin, Roie},
doi = {10.1137/1.9781611975994.94},
file = {:P\:/cloud_data/Projects/Paper/3381089.3381183.pdf:pdf},
isbn = {9781611975994},
journal = {Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms},
pages = {1525--1537},
title = {{The online submodular cover problem}},
volume = {2020-Janua},
year = {2020}
}
@article{Shi2021,
author = {Shi, Tianze and Malioutov, Igor and Bloomberg, L P and Benton, Adrian and Bloomberg, L P},
file = {:P\:/cloud_data/Projects/Paper/2021.naacl-main.207.pdf:pdf},
pages = {2616--2626},
title = {{Diversity-Aware Batch Active Learning for Dependency Parsing}},
year = {2021}
}
@article{Kothawadea,
archivePrefix = {arXiv},
arxivId = {arXiv:2210.01520v1},
author = {Kothawade, Suraj and Savarkar, Atharv and Iyer, Venkat and Tamil, Lakshman and Iyer, Rishabh},
eprint = {arXiv:2210.01520v1},
file = {:P\:/cloud_data/Projects/Paper/2210.01520.pdf:pdf},
title = {{CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification}}
}
@article{Beck,
archivePrefix = {arXiv},
arxivId = {arXiv:2402.13454v1},
author = {Beck, Nathan},
eprint = {arXiv:2402.13454v1},
file = {:P\:/cloud_data/Projects/Paper/2402.13454.pdf:pdf},
pages = {1--16},
title = {{T HEORETICAL A NALYSIS OF S UBMODULAR I NFORMATION}}
}
@article{Kothawade2021b,
abstract = {With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effectively solved at an additional labeling cost by targeted data subset selection(TSS) where a subset of unlabeled data points similar to an auxiliary set are added to the training data. We do so by using a rich class of Submodular Mutual Information (SMI) functions and demonstrate its effectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly, we compare the performance of SMI functions for TSS with other state-of-the-art methods for closely related problems like active learning. Using SMI functions, we observe $\sim$20-30% gain over the model's performance before re-training with added targeted subset; $\sim$12% more than other methods.},
archivePrefix = {arXiv},
arxivId = {2105.00043},
author = {Kothawade, Suraj and Kaushal, Vishal and Ramakrishnan, Ganesh and Bilmes, Jeff and Iyer, Rishabh},
eprint = {2105.00043},
file = {:P\:/cloud_data/Projects/Paper/2105.00043.pdf:pdf},
title = {{Submodular Mutual Information for Targeted Data Subset Selection}},
url = {http://arxiv.org/abs/2105.00043},
year = {2021}
}
@article{Yuan,
archivePrefix = {arXiv},
arxivId = {arXiv:2310.14277v1},
author = {Yuan, Bo and Zhao, Danpei},
eprint = {arXiv:2310.14277v1},
file = {:P\:/cloud_data/Projects/Paper/2310.14277.pdf:pdf},
number = {X},
pages = {1--20},
title = {{A Survey on Continual Semantic Segmentation : Theory , Challenge , Method and Application}},
volume = {X}
}
@inproceedings{Liu_2021_ICCV,
author = {Liu, Zhuoming and Ding, Hao and Zhong, Huaping and Li, Weijia and Dai, Jifeng and He, Conghui},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {oct},
pages = {9274--9283},
title = {{Influence Selection for Active Learning}},
year = {2021}
}
@inproceedings{10.1007/978-3-031-19839-7_1,
abstract = {Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc., however their success depends on the availability of a large amount of data from the domain of interest. While deep models perform well in terms of overall accuracy, they often struggle in performance on rare yet critical data slices. For e.g., detecting objects in rare data slices like ``motorcycles at night'' or ``bicycles at night'' for self-driving applications. Active learning (AL) is a paradigm to incrementally and adaptively build training datasets with a human in the loop. However, current AL based acquisition functions are not well-equipped to mine rare slices of data from large real-world datasets, since they are based on uncertainty scores or global descriptors of the image. We propose Talisman, a novel framework for Targeted Active Learning for object detectIon with rare slices using Submodular MutuAl iNformation. Our method uses the submodular mutual information functions instantiated using features of the region of interest (RoI) to efficiently target and acquire images with rare slices. We evaluate our framework on the standard PASCAL VOC07+12 [8] and BDD100K [31], a real-world large-scale driving dataset. We observe that Talisman consistently outperforms a wide range of AL methods by {\$}{\$}{\backslash}approx {\$}{\$}≈5{\%}- - 14{\%} in terms of average precision on rare slices, and {\$}{\$}{\backslash}approx {\$}{\$}≈2{\%}--4{\%} in terms of mAP. The code for Talisman is available here: https://github.com/surajkothawade/talisman.},
address = {Cham},
author = {Kothawade, Suraj and Ghosh, Saikat and Shekhar, Sumit and Xiang, Yu and Iyer, Rishabh},
booktitle = {Computer Vision -- ECCV 2022},
editor = {Avidan, Shai and Brostow, Gabriel and Ciss{\'{e}}, Moustapha and Farinella, Giovanni Maria and Hassner, Tal},
isbn = {978-3-031-19839-7},
pages = {1--16},
publisher = {Springer Nature Switzerland},
title = {{Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information}},
year = {2022}
}
@inproceedings{9607806,
abstract = {Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling. In this work, we propose a new pool-based method for active learning, which proposes promising patches extracted from full image, in each acquisition step. The problem is framed in an exploration-exploitation framework by combining an embedding based on Uniform Manifold Approximation to model representativeness with entropy as uncertainty measure to model informativeness. We applied our proposed method to the autonomous driving datasets CamVid and Cityscapes and performed a quantitative comparison with state-of-the-art baselines. We find that our active learning method achieves better performance compared to previous methods.},
address = {Los Alamitos, CA, USA},
author = {Sreenivasaiah, D and Otterbach, J and Wollmann, T},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
doi = {10.1109/ICCVW54120.2021.00120},
keywords = {manifolds;learning systems;image segmentation;unce},
month = {oct},
pages = {1029--1037},
publisher = {IEEE Computer Society},
title = {{MEAL: Manifold Embedding-based Active Learning}},
url = {https://doi.ieeecomputersociety.org/10.1109/ICCVW54120.2021.00120},
year = {2021}
}
@article{jimaging7020037,
abstract = {We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.},
author = {Saidu, Isah Charles and Csat{\'{o}}, Lehel},
doi = {10.3390/jimaging7020037},
issn = {2313-433X},
journal = {Journal of Imaging},
number = {2},
title = {{Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation}},
url = {https://www.mdpi.com/2313-433X/7/2/37},
volume = {7},
year = {2021}
}
@inproceedings{sener2018active,
author = {Sener, Ozan and Savarese, Silvio},
booktitle = {International Conference on Learning Representations},
title = {{Active Learning for Convolutional Neural Networks: A Core-Set Approach}},
url = {https://openreview.net/forum?id=H1aIuk-RW},
year = {2018}
}
@inproceedings{NEURIPS2021_4afe0449,
author = {Ash, Jordan and Goel, Surbhi and Krishnamurthy, Akshay and Kakade, Sham},
booktitle = {Advances in Neural Information Processing Systems},
editor = {Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, P S and Vaughan, J Wortman},
pages = {8927--8939},
publisher = {Curran Associates, Inc.},
title = {{Gone Fishing: Neural Active Learning with Fisher Embeddings}},
url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/4afe044911ed2c247005912512ace23b-Paper.pdf},
volume = {34},
year = {2021}
}
@inproceedings{pmlr-v70-gal17a,
abstract = {Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).},
author = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
editor = {Precup, Doina and Teh, Yee Whye},
pages = {1183--1192},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Deep {B}ayesian Active Learning with Image Data}},
url = {https://proceedings.mlr.press/v70/gal17a.html},
volume = {70},
year = {2017}
}
@inproceedings{inproceedings,
author = {Neven, Robby and Goedem{\'{e}}, Toon},
booktitle = {Conference: 18th International Conference on Computer Vision Theory and Applications},
doi = {10.5220/0011680100003417},
pages = {95--104},
title = {{Uncertainty-Aware DPP Sampling for Active Learning}},
year = {2023}
}
@inproceedings{Ash2020Deep,
author = {Ash, Jordan T and Zhang, Chicheng and Krishnamurthy, Akshay and Langford, John and Agarwal, Alekh},
booktitle = {International Conference on Learning Representations},
title = {{Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds}},
url = {https://openreview.net/forum?id=ryghZJBKPS},
year = {2020}
}
@inproceedings{32948baba85d4c19b9d9594dcf57bf03,
annote = {2020 International Conference on Machine Learning : Workshop on Uncertainty and Robustness in Deep Learning ; Conference date: 12-07-2020 Through 18-07-2020},
author = {Li, Bo and Alstr{\o}m, Tommy Sonne},
booktitle = {Proceedings of 2020 International Conference on Machine Learning: Workshop on Uncertainty and Robustness in Deep Learning},
title = {{On uncertainty estimation in active learning for image segmentation}},
year = {2020}
}
@article{kirsch2022unifying,
annote = {Expert Certification},
author = {Kirsch, Andreas and Gal, Yarin},
issn = {2835-8856},
journal = {Transactions on Machine Learning Research},
title = {{Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities}},
url = {https://openreview.net/forum?id=UVDAKQANOW},
year = {2022}
}
@article{Tu2016ReducingTM,
author = {Tu, Ming and Berisha, Visar and Cao, Yu and Seo, Jae-sun},
journal = {2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)},
pages = {93--98},
title = {{Reducing the Model Order of Deep Neural Networks Using Information Theory}},
url = {https://api.semanticscholar.org/CorpusID:9671800},
year = {2016}
}
@inproceedings{10030143,
author = {Ji, Yilin and Kaestner, Daniel and Wirth, Oliver and Wressnegger, Christian},
booktitle = {2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
doi = {10.1109/WACV56688.2023.00393},
keywords = {Deep learning;Computer vision;Neural networks;Stab},
pages = {3932--3941},
title = {{Randomness is the Root of All Evil: More Reliable Evaluation of Deep Active Learning}},
year = {2023}
}
@article{Shen2021,
abstract = {Over the last decade, deep learning methods have achieved state-of-the-art for medical image segmentation tasks. However, the difficulty of obtaining sufficient labeled data can be a bottleneck. To this end, we design a novel active learning framework specially adapted to the brain tumor segmentation. Our approach includes a novel labeling cost designed to capture radiologists' practical labeling costs. This is combined with two acquisition functions to incorporate uncertainty and representation information, ensuring that the active learning selects informative and diverse data. The resulting procedure is a constrained combinatorial optimization problem. We propose an efficient algorithm for this task and demonstrate the proposed method's advantages for segmenting brain MRI data.},
author = {Shen, Maohao and Zhang, Jacky Y. and Chen, Leihao and Yan, Weiman and Jani, Neel and Sutton, Brad and Koyejo, Oluwasanmi},
doi = {10.1109/ISBI48211.2021.9434098},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/isbi48211.2021.9434098.pdf:pdf},
isbn = {9781665412469},
issn = {19458452},
journal = {Proceedings - International Symposium on Biomedical Imaging},
keywords = {Active Learning,Approximation Algorithm,Deep Learning,Segmentation,Uncertainty},
pages = {1269--1273},
title = {{Labeling cost sensitive batch active learning for brain tumor segmentation}},
volume = {2021-April},
year = {2021}
}
@article{Ji,
author = {Ji, Yilin and Kaestner, Daniel and Wirth, Oliver and Wressnegger, Christian},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2023-wacv.pdf:pdf},
number = {c},
title = {{Randomness is the Root of All Evil : More Reliable Evaluation of Deep Active Learning}}
}
@article{,
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2088_hybrid_active_learning_with_un.pdf:pdf},
number = {2019},
pages = {1--17},
title = {{Hybrid Active Learning with Uncertainty-Weighted Embed- dings}},
year = {2023}
}
@article{Yoo2019LearningLF,
author = {Yoo, Donggeun and Kweon, In-So},
file = {:P\:/cloud_data/Projects/Paper/1905.03677.pdf:pdf},
journal = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {93--102},
title = {{Learning Loss for Active Learning}},
url = {https://api.semanticscholar.org/CorpusID:148571749},
year = {2019}
}
@article{9310359,
author = {Nath, Vishwesh and Yang, Dong and Landman, Bennett A and Xu, Daguang and Roth, Holger R},
doi = {10.1109/TMI.2020.3048055},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Image segmentation;Biomedical imaging;Deep learnin},
number = {10},
pages = {2534--2547},
title = {{Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation}},
volume = {40},
year = {2021}
}
@article{Xia2023,
author = {Xia, Meng and Henao, Ricardo},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/1364_reliable_active_learning_via_i.pdf:pdf},
pages = {1--26},
title = {{Reliable Active Learning via Influence Functions}},
year = {2023}
}
@article{Thorndike1953,
author = {Thorndike, Robert L.},
doi = {10.1007/BF02289263},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/BF02289263.pdf:pdf},
issn = {00333123},
journal = {Psychometrika},
number = {4},
pages = {267--276},
title = {{Who belongs in the family?}},
volume = {18},
year = {1953}
}
@article{Soen2021,
abstract = {In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories. The exact FIM is either unavailable in closed form or too expensive to compute. In practice, it is almost always estimated based on empirical samples. We investigate two such estimators based on two equivalent representations of the FIM - both unbiased and consistent. Their estimation quality is naturally gauged by their variance given in closed form. We analyze how the parametric structure of a deep neural network can affect the variance. The meaning of this variance measure and its upper bounds are then discussed in the context of deep learning.},
archivePrefix = {arXiv},
arxivId = {2107.04205},
author = {Soen, Alexander and Sun, Ke},
eprint = {2107.04205},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/NeurIPS-2021-on-the-variance-of-the-fisher-information-for-deep-learning-Paper.pdf:pdf},
isbn = {9781713845393},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {2},
pages = {5708--5719},
title = {{On the Variance of the Fisher Information for Deep Learning}},
volume = {7},
year = {2021}
}
@misc{DKFZ,
author = {(DKFZ), Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center},
keywords = {nnU-Net},
mendeley-tags = {nnU-Net},
title = {{nnUNet}},
url = {https://github.com/MIC-DKFZ/nnUNet}
}
@article{Hacohen2023a,
archivePrefix = {arXiv},
arxivId = {arXiv:2306.03543v2},
author = {Hacohen, Guy and Weinshall, Daphna},
eprint = {arXiv:2306.03543v2},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2306.03543.pdf:pdf},
number = {Section 3},
title = {{How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget arXiv : 2306 . 03543v2 [ cs . LG ] 23 Oct 2023}},
year = {2023}
}
@article{Kothawade,
author = {Kothawade, Suraj and Ghosh, Saikat and Shekhar, Sumit and Xiang, Yu},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2112.00166.pdf:pdf},
keywords = {ance,class imbal-,object detection,rare slices,submodular mutual information,targeted active learning},
title = {{TALISMAN : Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information}}
}
@article{Neven2023,
author = {Neven, Robby},
doi = {10.5220/0011680100003417},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/116801.pdf:pdf},
isbn = {0000000308571},
number = {Visigrapp},
pages = {95--104},
title = {{Uncertainty-Aware DPP Sampling for Active Learning}},
volume = {4},
year = {2023}
}
@article{Byk,
archivePrefix = {arXiv},
arxivId = {arXiv:1906.07975v1},
author = {Bıyık, Erdem and Wang, Kenneth},
eprint = {arXiv:1906.07975v1},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/1906.07975.pdf:pdf},
title = {{Using Determinantal Point Processes}}
}
@article{,
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/KOTHAWADE-PRIMARY-2023.pdf:pdf},
title = {{No Title}}
}
@article{Tu,
archivePrefix = {arXiv},
arxivId = {arXiv:1605.04859v1},
author = {Tu, Ming and Berisha, Visar and Cao, Yu and Seo, Jae-sun},
eprint = {arXiv:1605.04859v1},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/1605.04859v1.pdf:pdf},
title = {{Reducing the Model Order of Deep Neural Networks Using Information Theory}}
}
@article{Kulesza,
archivePrefix = {arXiv},
arxivId = {arXiv:1207.6083v4},
author = {Kulesza, Alex and Jan, M L},
eprint = {arXiv:1207.6083v4},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/1207.6083.pdf:pdf},
pages = {1--120},
title = {{Determinantal point processes for machine learning}}
}
@article{Bateni2018,
author = {Bateni, Mohammadhossein},
doi = {10.1145/3219819.3220081},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/3219819.3220081.pdf:pdf},
isbn = {9781450355520},
pages = {1138--1147},
title = {{Optimal Distributed Submodular Optimization via Sketching}},
year = {2018}
}
@article{Xie,
archivePrefix = {arXiv},
arxivId = {arXiv:2010.08705v1},
author = {Xie, Shuai and Feng, Zunlei and Chen, Ying and Sun, Songtao},
eprint = {arXiv:2010.08705v1},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2010.08705.pdf:pdf},
keywords = {active learning,difficulty-aware,semantic segmentation},
pages = {1--17},
title = {{DEAL: Difficulty-aware Active Learning for Semantic Segmentation}}
}
@article{Liu,
author = {Liu, Zhuoming and Ding, Hao and Zhong, Huaping and Li, Weijia and Dai, Jifeng and He, Conghui},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2108.09331.pdf:pdf},
title = {{Influence Selection for Active Learning}}
}
@article{Zhu2022,
author = {Zhu, Tao and Zhao, Zhe and Liu, Weijie and Liu, Jiachi and Chen, Yiren and Mao, Weiquan and Liu, Haoyan},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2022.naacl-industry.35.pdf:pdf},
pages = {315--323},
title = {{Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System}},
year = {2022}
}
@article{Desalvo,
archivePrefix = {arXiv},
arxivId = {arXiv:2309.16139v1},
author = {Desalvo, Giulia},
eprint = {arXiv:2309.16139v1},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2309.16139.pdf:pdf},
title = {{T -s a l i s u d s}}
}
@article{Killamsetty2021,
abstract = {The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framework, GRAD-MATCH, which finds subsets that closely match the gradient of the training or validation set. We find such subsets effectively using an orthogonal matching pursuit algorithm. We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework. We show that GRAD-MATCH significantly and consistently outperforms several recent data-selection algorithms and achieves the best accuracy-efficiency trade-off. GRAD-MATCH is available as a part of the CORDS toolkit: https://github.com/decile-team/cords.},
archivePrefix = {arXiv},
arxivId = {2103.00123},
author = {Killamsetty, Krishnateja and Sivasubramanian, Durga and Ramakrishnan, Ganesh and De, Abir and Iyer, Rishabh},
eprint = {2103.00123},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2103.00123.pdf:pdf},
isbn = {9781713845065},
issn = {26403498},
journal = {Proceedings of Machine Learning Research},
pages = {5464--5474},
title = {{GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training}},
volume = {139},
year = {2021}
}
@article{Kothawade2022,
abstract = {With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i) targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is underperforming, and ii) guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. Through novel functions and their parameterizations, PRISM offers a variety of modeling capabilities that enable a trade-off between desired qualities of a subset like diversity or representation and similarity/dissimilarity with a set of data points. We demonstrate how PRISM can be applied to the two real-world problems mentioned above, which require guided subset selection. In doing so, we show that PRISM interestingly generalizes some past work, therein reinforcing its broad utility. Through extensive experiments on diverse datasets, we demonstrate the superiority of PRISM over the state-of-the-art in targeted learning and in guided imagecollection summarization.},
archivePrefix = {arXiv},
arxivId = {2103.00128},
author = {Kothawade, Suraj and Kaushal, Vishal and Ramakrishnan, Ganesh and Bilmes, Jeff and Iyer, Rishabh},
doi = {10.1609/aaai.v36i9.21264},
eprint = {2103.00128},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2103.00128.pdf:pdf},
isbn = {1577358767},
issn = {2159-5399},
journal = {Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022},
number = {i},
pages = {10238--10246},
title = {{PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection}},
volume = {36},
year = {2022}
}
@article{Gaillochet2023,
abstract = {The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.},
archivePrefix = {arXiv},
arxivId = {2301.07670},
author = {Gaillochet, M{\'{e}}lanie and Desrosiers, Christian and Lombaert, Herv{\'{e}}},
doi = {10.1016/j.media.2023.102958},
eprint = {2301.07670},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2301.07670.pdf:pdf},
issn = {13618423},
journal = {Medical Image Analysis},
keywords = {Active learning,Medical image analysis,Segmentation,Uncertainty},
title = {{Active learning for medical image segmentation with stochastic batches}},
volume = {90},
year = {2023}
}
@article{Yu2023,
abstract = {Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure. Active learning addresses this challenge by striving for optimum performance with minimal labeling cost by selecting the most informative and representative images for labeling. Despite its potential, active learning has been less explored in instance segmentation compared to other tasks like image classification, which require less labeling. In this study, we propose a post-hoc active learning algorithm that integrates uncertainty-based sampling with diversity-based sampling. Our proposed algorithm is not only simple and easy to implement, but it also delivers superior performance on various datasets. Its practical application is demonstrated on a real-world overhead imagery dataset, where it increases the labeling efficiency fivefold.},
archivePrefix = {arXiv},
arxivId = {2309.16139},
author = {Yu, Ke and Albro, Stephen and DeSalvo, Giulia and Kothawade, Suraj and Rashwan, Abdullah and Tavakkol, Sasan and Batmanghelich, Kayhan and Yin, Xiaoqi},
eprint = {2309.16139},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2309.16139v1.pdf:pdf},
title = {{Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling}},
url = {http://arxiv.org/abs/2309.16139},
year = {2023}
}
@article{Kaushal2022,
abstract = {Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.},
archivePrefix = {arXiv},
arxivId = {2202.10680},
author = {Kaushal, Vishal and Ramakrishnan, Ganesh and Iyer, Rishabh},
eprint = {2202.10680},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2202.10680.pdf:pdf},
title = {{Submodlib: A Submodular Optimization Library}},
url = {http://arxiv.org/abs/2202.10680},
year = {2022}
}
@article{Kwon2022,
abstract = {Pruning is an effective way to reduce the huge inference cost of Transformer models. However, prior work on pruning Transformers requires retraining the models. This can add high training cost and high complexity to model deployment, making it difficult to use in many practical situations. To address this, we propose a fast post-training pruning framework for Transformers that does not require any retraining. Given a resource constraint and a sample dataset, our framework automatically prunes the Transformer model using structured sparsity methods. To retain high accuracy without retraining, we introduce three novel techniques: (i) a lightweight mask search algorithm that finds which heads and filters to prune based on the Fisher information; (ii) mask rearrangement that complements the search algorithm; and (iii) mask tuning that reconstructs the output activations for each layer. We apply our method to BERTBASE and DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD benchmarks. Our framework achieves up to 2.0× reduction in FLOPs and 1.56× speedup in inference latency, while maintaining < 1% loss in accuracy. Importantly, our framework prunes Transformers in less than 3 minutes on a single GPU, which is over two orders of magnitude faster than existing pruning approaches that retrain the models.},
archivePrefix = {arXiv},
arxivId = {2204.09656},
author = {Kwon, Woosuk and Kim, Sehoon and Mahoney, Michael W. and Hassoun, Joseph and Keutzer, Kurt and Gholami, Amir},
eprint = {2204.09656},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2204.09656.pdf:pdf},
isbn = {9781713871088},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {NeurIPS},
pages = {1--21},
title = {{A Fast Post-Training Pruning Framework for Transformers}},
volume = {35},
year = {2022}
}
@article{Kothawade2021,
abstract = {Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in realistic scenarios such as imbalance or rare classes, out-of-distribution data in the unlabeled set, and redundancy. In this work, we propose SIMILAR (Submodular Information Measures based actIve LeARning), a unified active learning framework using recently proposed submodular information measures (SIM) as acquisition functions. We argue that SIMILAR not only works in standard active learning but also easily extends to the realistic settings considered above and acts as a one-stop solution for active learning that is scalable to large real-world datasets. Empirically, we show that SIMILAR significantly outperforms existing active learning algorithms by as much as ≈ 5% - 18% in the case of rare classes and ≈ 5% -10% in the case of out-of-distribution data on several image classification tasks like CIFAR-10, MNIST, and ImageNet. SIMILAR is available as a part of the DISTIL toolkit: https://github.com/decile-team/distil.},
archivePrefix = {arXiv},
arxivId = {2107.00717},
author = {Kothawade, Suraj and Beck, Nathan and Killamsetty, Krishnateja and Iyer, Rishabh},
eprint = {2107.00717},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2107.00717.pdf:pdf},
isbn = {9781713845393},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {c},
pages = {18685--18697},
title = {{SIMILAR: Submodular Information Measures Based Active Learning in Realistic Scenarios}},
volume = {23},
year = {2021}
}
@article{Kothawade2022a,
abstract = {Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc., however their success depends on the availability of a large amount of data from the domain of interest. While deep models perform well in terms of overall accuracy, they often struggle in performance on rare yet critical data slices. For e.g., detecting objects in rare data slices like “motorcycles at night” or “bicycles at night” for self-driving applications. Active learning (AL) is a paradigm to incrementally and adaptively build training datasets with a human in the loop. However, current AL based acquisition functions are not well-equipped to mine rare slices of data from large real-world datasets, since they are based on uncertainty scores or global descriptors of the image. We propose Talisman, a novel framework for Targeted Active Learning for object detectIon with rare slices using Submodular MutuAl iNformation. Our method uses the submodular mutual information functions instantiated using features of the region of interest (RoI) to efficiently target and acquire images with rare slices. We evaluate our framework on the standard PASCAL VOC07+12 [8] and BDD100K [31], a real-world large-scale driving dataset. We observe that Talisman consistently outperforms a wide range of AL methods by ≈ 5%- - 14% in terms of average precision on rare slices, and ≈ 2%–4% in terms of mAP. The code for Talisman is available here: https://github.com/surajkothawade/talisman.},
archivePrefix = {arXiv},
arxivId = {2112.00166},
author = {Kothawade, Suraj and Ghosh, Saikat and Shekhar, Sumit and Xiang, Yu and Iyer, Rishabh},
doi = {10.1007/978-3-031-19839-7_1},
eprint = {2112.00166},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/136980001.pdf:pdf},
isbn = {9783031198380},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Class imbalance,Object detection,Rare slices,Submodular mutual information,Targeted active learning},
pages = {1--16},
title = {{Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information}},
volume = {13698 LNCS},
year = {2022}
}
@article{Foellmer,
author = {Foellmer, Bernhard and Wald, Christian and Biavati, Federico and Stober, Sebastian and Nakajima, Shinichi and Ma, Jackie and Dewey, Marc and Samek, Wojciech},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/XAL_manuscript_20230219.pdf:pdf},
title = {{Explainable Active Learning with Uncertainty aware Fisher for Medical Image Segmentation}}
}
@article{Wei2015,
abstract = {We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme called filtered active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including deep neural network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers.},
author = {Wei, Kai and Iyer, Rishabh and Bilmes, Jeff},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/wei15.pdf:pdf},
isbn = {9781510810587},
journal = {32nd International Conference on Machine Learning, ICML 2015},
pages = {1954--1963},
title = {{Submodularity in data subset selection and active learning}},
volume = {3},
year = {2015}
}
@article{Economics2020,
abstract = {Purpose: Corporate governance (CG) is an ongoing interesting topic getting the attention of market participant, business regulators and researchers in today's business environment. The purpose of this study is to analyze the moderating role of earnings management on CG-value and CG-risk relationship in the emerging economy of Pakistan. Design/methodology/approach: A panel data analysis is used in this study. A panel data of 71 non-financial listed companies of Pakistan for the 2008-2017 period is considered for this study. Secondary data is collected from the annual reports of non-financial firms listed on PSX. Seven econometric equations are developed to test the research hypothesis. Findings: The results reveal that CG significantly enhances the firm value and performance measures. Moreover, CG mitigates the practices of earning management and eliminates the risk that develops opportunistic behavior among managers to commit frauds. Practical implications: The results of this study suggest that the board of directors (BODs) should intensify their governance role and ensure that the executives perform their duties to maximize the wealth of the shareholders and not engage in any misrepresentation of accounts that may lower the company position and decrease the firm value. Moreover, the managers should be informed about their accountability and acknowledged that at the end of the year, they would be audited by an expert's auditors for their responsibilities. Concerning regulatory bodies, regulatory authorities should ensure that there must be at least one independent member on the board. The better-governed system reduces both agency conflicts and enhances firm value. Originality/value: A number of studies have already been undertaken by multiple investigators to build connection among CG with firm performance, but there is not even a single study in the literature that considers CG, firm value, firm Risk and discretionary earning management as a whole in one model to generalize its results in the emerging economy of Pakistan. A fundamental element of current analyzation process addresses that this is the very first graft of study conducted in Pakistan having combination of four variables together in one revision. There is minimal work that focuses on moderating effects of earning management on the CG-value and CG-risk relationships. This study uses two standard measures of firm performance (i.e. ROA and Tobin's Q), one proxy of earning management (DEM) and three attributes of CG (board size, audit quality and ownership structure). Previously, researchers have not investigated a model that combines variables (CG as independent and Firm performance and Firm Risk as dependent along with DEM as moderator) in a single study.},
author = {Economics, Procedia and Khaldoon, Albitar and Ahmad, Alqatan and Wei, Huang and Yousaf, Imran and Ali, Shuja Shoaib and Naveed, Muhammad and Latif, Aysha S and Abdullah, Fahad and {Ab Razak}, Nazrul Hisyam and Palahuddin, Salmi Huwaina and {Tasneem Sajjad , Nasir Abbas, Shahzad Hussain, SabeehUllah}, Abdul Waheed and Gulzar, M. Awais and Zongjun, Wang and Gunderson, Michael and Gloy, Brent and Rodgers, Chris and Orazalin, Nurlan and Mahmood, Monowar and {Jung Lee}, Keun and Sial, Muhammad Safdar and Chunmei, Zheng and Khuong, Nguyen Vinh and Prashar, Anupama and Gupta, Parul and Shawtari, Fekri Ali and {Har Sani Mohamad}, Muslim and {Abdul Rashid}, Hafiz Majdi and Ayedh, Abdullah Moh'd and Shahid, Muhammad Naeem and Abbas, Aamir and Latif, Khalid and Attique, Ayesha and Khalid, Safwan and Ownership, Corporate and Ould, Nejla and Ellili, Daoud and Asim, Aysha and Ismail, Aisha and Nosheen, Safia and Ul, Naveed and Shahzad, Khurram and Pernamasari, Rieke and {Nur Fitria}, Giawan and Ahmad, Noor Hazlina and Ghafar, Abdul and Nair, Malini and AL-Duais, Shaker Dahan and Malek, Mazrah and Hamid, Mohamad Ali Abdul and Aman, Nida and Jan, Farzand Ali and Ullah, Naeem and Mujtaba, Ammara and Mohamad, Shafi and Abdurrahman, Adamu Pantamee and Keong, Ooi Chee and Garrett, Kwong Wing Chong and Nalarreason, Kadek Marlina and T, Sutrisno and Mardiati, Endang and Salim, M. Noor and HN, M. Rusman and Moslemany, Racha El and Nathan, Demyana and Kesaulya, Feby Astrid and Putri, Weny and Sri, Dewi and Sanad, Zakeya and Shiwakoti, Radha and Kukreja, Gagan and Kahtani, Nouf Al and Garc{\'{i}}a-Meca, Emma and S{\'{a}}nchez-Ballesta, Juan P. and Steel, Reilly S and Columbia, Source and Review, Law and January, No and Steel, Reilly S and Baig, Maliha and Ali, Shuja Shoaib and Nanda, Ulfa Luthfia and Somantri, Yeni Fitriani and Yeni, Fitri and Melmusi, Zerni and Wulandari, Esti and Ilona, Desi and Brooks, Leonard J. and Obigbemi, Imoleayo F. and Omolehinwa, Eddy O. and Mukoro, Dick Oluku and Ben-Caleb, Egbide and Olusanmi, Olamide Adeola and Attarit, Thanapin and Accounting, Australasian and Abdulrasool, Lec and Saleem, S M Al-Diler and Bala, Hussaini and Kumai, Gugong Benjamin and Buniamin, Sharifah and Johari, Nor Hasimah and Raida, Noor and Rahman, Abd and Hashim, Hafiza Aishah and Devi, S Susela and Liu, Jo-lan and Tsai, Ching-chieh and Sarkar, Jayati and Soliman, Mohamed Moustafa and Ragab, Aiman Ahmed and Bradbury, Michael E and Mak, Yuen Teen and Kayode, Emmanuel and Kharashgah, Khaled Ahmad and Afza, Noor and Amran, Binti and Ishak, Rokiah Binti and {Australian Stock Exchange} and Abbadi, Sinan S. and Hijazi, Qutaiba F. and Al-Rahahleh, Ayat S. and Aygun, M. A. and Ic, S. and Arvas, M. A. and {Dwi Lusi Tyasing Swastika}, Dwi Lusi Tyasing Swastika and Saftiana, Yulia and Mukhtaruddin and Putri, Krisna Winda and Ferina, Ika Sasti and Srivastava, Smita Saurabh and Zouhayer, Mighri and Rahman, Rashidah Abdul and {Mohamed Ali}, Fairuzana Haneem and Shahwan, Yousef and Almubaydeen, Tareq Hammad and Lev, Baruch and Srivastava, Anup and Saleem, Faiza and Alifiah, Mohd Norfian and Tahir, Muhammad Sohail and Jeon, Kyeongmin and Peni, Emilia and V{\"{a}}h{\"{a}}maa, Sami and Ruparatne, C. H. H. K. M. P. and Hemathilake, D. H. U. and Meegaswatte, D. M. K. K. and Chandrasiri, S. M. N. and Fernando, W. H. L. and Sanjaya, W. H. H. and Wannigama, K. G. and Ravindra, M. G. K. and Harshani, H. M. G. and Liyanage, G. H. and Srivastava, Ritu and Cheema, Surbhi and Bassiouny, Sara W and Accounting, Managerial and Feshaki, Alireza Ghonji and Khanmohammadi, Mohammad Hamed and Branch, Damavand and Yazdani, Shohreh and Branch, Damavand and Kumari, Prity and Pattanayak, J K and Sehrawat, Neeraj K. and Kumar, Amit and Lohia, Nandita and Bansal, Satvik and Agarwal, Tanya and Adamu, Adamu Idris and Ishak, Rokiah Binti and Hassan, Nor Laili and Shen, Chung Hua and Chih, Hsiang Lin and Alareeni, Bahaaeddin and Waweru, Nelson M and Riro, George K and Adom, Dickson and Hussein, Emad Kamil and Kj{\ae}rland, Frode and Haugdal, Ane Tolnes and S{\o}ndergaard, Anna and V{\aa}gslid, Anne and Lin, Jerry W. and Li, June F. and Yang, Joon S. and {Yazar Soyadı}, Edi and Shuaibu, Hussaini and Amin, Aminul and Miko, N.U. and Kecerdasan, Inventori and Ikep, Pelbagai and Audit, External and Companies, Listed and Ibrahim, Yousef and Abu, Suleiman and Faizal, Khairil and Khairi, Bin and Srivastava, Anup and Majeed, Muhammad Kashif and Jun, Ji Cheng and Zia-Ur-Rehman, Muhammad and Mohsin, Muhammad and Rafiq, Muhammad Zeeshan and Zahid, Muhammad and Piosik, Andrzej and Genge, Ewa and Issn, Accounting and Uwuigbe, Uwalomwa and Sunday, Daramola and Oyeniyi, Anjolaoluwa and Hanim, Fatimah and Tenaga, Universiti and Buniamin, Sharifah and Tenaga, Universiti and Raida, Noor and Tenaga, Universiti and Abd, Norliza and Rauf, Abdul and Johari, Nor Hasimah and Raida, Noor and Rahman, Abd and Rauf, Abdul and Rahman, Abd and Shah, Attaullah and Roodposhti, F Rahnamay and Chashmi, S A Nabavi and Patrick, Egbunike Amaechi and Paulinus, Ezelibe Chizoba and Nympha, Aroh Nkechi and Ullah, Kamran and Bagh, Tanveer and {Al Azeez}, Hosam Alden Riyadh and Sukoharsono, Eko Ganis and Roekhudin and Andayani, Wuryan and Hashed, Abdulwahid A. and Almaqtari, Faozi A. and Amran, Noor Afza and Ishak, Rokiah Binti and {Abdul Manaf}, Kamarul Bahrain and Hassan, Shehu Usman and {Ahmed, AbubakarHassan, S. U.}, & and Busirin, Mohd Fazrin and Zakaria, N O R Balkish and Azmi, Nurul Azlin and Hermawan, Acella and Iraya, Cyrus and Mwangi, Mirie and Muchoki, G.W and Srivastava, Smita Saurabh and Singh, Shiwangi and Dhir, Sanjay and Muda, Iskandar and Maulana, Weldi and Siregar, Hasan Sakti and Indra, Naleni and {Blkasem Elhaj-}, Elhusin and Miko, N.U. and Sampieri, Roberto Hernandez and Asghar, Affaf and Sajjad, Seemab and Shahzad, Aamer and Matemilola, Bolaji Tunde and Altarawneh, Marwan and Shafie, Rohami and Ishak, Rokiah Binti},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/PhD_Thesis_Fabian_submitted.pdf:pdf},
isbn = {1120190347},
issn = {14720701},
journal = {Corporate Governance (Bingley)},
keywords = {2015,2015 the authors,2019,971 50 7766392,A company's ownership structure,AEM,Accrual earnings management (AEM),Accrual-based earnings management (AEM),Agency Theory,Agency theory,Audit committee,Audit committee independence,Audit committees,Bank performance,Board characteristics,Board composition,Board diversity,Board independence,Board of Directors,Board of directors,Board size,Board structure,Boards of Directors,Boards of directors,Business enterprises -- Canada,CEO characteristics,CEO duality,CEO-chair duality,Canada,Chief executives,Co-operative organizations,Commercial banks,Compensation,Competitive strategy,Compliance with IFRS,Composition of commissioners,Corporat Governance,Corporate,Corporate Governance,Corporate disclosure,Corporate diversification,Corporate finances,Corporate governance,Corporate governance (CG),Corporate governance mechanisms,Corporate governance quality,Corporate governance reforms,Corporate social responsibility (CSR),Country of origin,Credit,Credit crisis,Cultural distance,Culture,Debt Covenant Restrictions,Department of Business Administration (UAJ&K),Development,Discretionary Earning Management,Discretionary accruals,Distressed and Non-Distressed Firms,Dividend Payout,Dividend policy,Earning management,Earning management and public listed firm,Earnings,Earnings Management,Earnings management,Earnings management (EM),Earnings quality,Economic performance,Emerging economies,Emerging markets,Expectations management,External directors,Faculty of Management Sciences,Family ownership,Farms,Female directors,Finance and Management Scholar at Riphah Internati,Financial Reporting,Financial crisis,Financial disclosure,Financial performance,Financial reporting,Financial reporting quality,Financial restatements,Financial transparency,Firm Characteristics,Firm performance,Firm size,Fixed Effect Model JEL Classification: Correspondi,Foreign ownership,G11,Gender integration,General and administrative expenses,Good corporate governance,Governance,Growth investing,Hedged portfolio,IFRS adoption,Incentives and Constraints,Income inequality gap,Independent directors,India,Institutional investors,Intangible investments,Intangibles,Internal ownership,International business,Intra-industry homogeneity,Investor protection,JEL classification: E32,Kazakhstan,Lecturer Banking Finance and Accounting at Indus G,Leverage,Liquidity,M41 Keywords: Value investing,MBA Finance,Malaysia,Managerial ownership,Market to book ratio,Meta-Analysis,Modified Jones (1995) model,National culture,Ownership Issues,Ownership Structure,Ownership concentration,Ownership structures,Pakistan,Profitability,Quantile regression,R and D expenses,R&D,REM,Real earning management,Real earnings management,Real earnings management (REM),Real performance,Return on assets (ROA),Riphah School of Leadership,Rururbanisation,Russia,Saudi GAAP,Selling,Systematic review,TCCM framework,Tax Avoidance and Institutional Ownership,Tobin's 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number = {1},
pages = {54--75},
title = {{No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title}},
volume = {10},
year = {2020}
}
@article{Killamsetty2021a,
abstract = {Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (with hyperparameter tuning) requires significant computing resources and time. Secondly, real-world data is noisy and imbalanced. As a result, several recent papers try to make the training process more efficient and robust. However, most existing work either focuses on robustness or efficiency, but not both. In this work, we introduce GLISTER, a GeneraLIzation based data Subset selecTion for Efficient and Robust learning framework. We formulate GLISTER as a mixed discrete-continuous bi-level optimization problem to select a subset of the training data, which maximizes the log-likelihood on a held-out validation set. We then analyze GLISTER for simple classifiers such as gaussian and multinomial naive-bayes, k-nearest neighbor classifier, and linear regression and show connections to submodularity. Next, we propose an iterative online algorithm GLISTER-ONLINE, which performs data selection iteratively along with the parameter updates and can be applied to any loss-based learning algorithm. We then show that for a rich class of loss functions including cross-entropy, hinge-loss, squared-loss, and logistic-loss, the inner discrete data selection is an instance of (weakly) submodular optimization, and we analyze conditions for which GLISTER-ONLINE reduces the validation loss and converges. Finally, we propose GLISTER-ACTIVE, an extension to batch active learning, and we empirically demonstrate the performance of GLISTER on a wide range of tasks including, (a) data selection to reduce training time, (b) robust learning under label noise and imbalance settings, and (c) batch-active learning with several deep and shallow models. We show that our framework improves upon state of the art both in efficiency and accuracy (in cases (a) and (c)) and is more efficient compared to other state-of-the-art robust learning algorithms in case (b). The code for GLISTERis at:https://github.com/dssresearch/GLISTER.},
archivePrefix = {arXiv},
arxivId = {2012.10630},
author = {Killamsetty, Krishnateja and Sivasubramanian, Durga and Ramakrishnan, Ganesh and Iyer, Rishabh},
doi = {10.1609/aaai.v35i9.16988},
eprint = {2012.10630},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/2012.10630.pdf:pdf},
isbn = {9781713835974},
issn = {2159-5399},
journal = {35th AAAI Conference on Artificial Intelligence, AAAI 2021},
pages = {8110--8118},
title = {{GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning}},
volume = {9B},
year = {2021}
}
@article{Settles2009,
abstract = {Settles, Burr. Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, 2009.},
author = {Settles, Burr},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/TR1648.pdf:pdf},
keywords = {active learning,literature surv,machine learning},
number = {January},
title = {{Computer Sciences Active Learning Literature Survey}},
year = {2009}
}
@book{SixiaoZheng2023,
author = {{Sixiao Zheng} and {Jiachen Lu} and {Hengshuang Zhao} and {Li Zhang}},
doi = {10.1007/978-3-031-19839-7},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/978-3-031-19839-7.pdf:pdf},
isbn = {978-3-031-25065-1},
title = {{Computer Vision – ECCV 2022 Workshops}},
url = {https://link.springer.com/10.1007/978-3-031-25066-8},
volume = {13803},
year = {2023}
}
@article{Meyer2018,
abstract = {There are so many interesting things about Fisher Information [1] and its theoretical prop-erties (and for that matter, its applications), but I'll start here with definitions. Let {P $\theta$ } $\theta$∈$\Theta$ denote a parametric family of distributions on a space X , where each $\theta$ ∈ $\Theta$ ⊂ R d indexes {P $\theta$ }. Assume (with no real loss of generality, AFAICT) that each P $\theta$ has a density p $\theta$ . Then the Fisher information associated with the model is the matrix given by I $\theta$ := E $\theta$ ∇ $\theta$ log p $\theta$ (X)∇ $\theta$ log p $\theta$ (X) T = E $\theta$ [l $\theta$ l T $\theta$ ] (1) where l $\theta$ is the score function ∇ $\theta$ log p $\theta$ (X). The score function turns up in perhaps unex-pected places (e.g., score function gradient in policy gradient reinforcement learning) and has various applications. Essentially the Fisher Information tells us how much information a random variable X contains about the parameter vector $\theta$, where X is distributed according to a probability distribution parameterized by $\theta$. Intuitively, the Fisher information captures the variability of the gradient of the score function, ∇ $\theta$ log p $\theta$ (x); in a family of distributions for which the score function has high variability, we expect estimation of the parameter $\theta$ to be easier; essentially (and perhaps counter-Intuitively) events with lower probability contain more information. Fisher Information can be described in various ways. One way to think about the Fisher Information is that it is the variance of the score function l $\theta$ . Recall that the mean µ = E[X] and the variance $\sigma$ 2 = E[(X − µ) 2 ]. First, consider E ∇ $\theta$ log p $\theta$ (X) = E l $\theta$ .},
author = {Meyer, David},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/fisher.pdf:pdf},
pages = {1--11},
title = {{A Few Notes on Fisher Information (WIP)}},
year = {2018}
}
@article{Gaillochet2022,
abstract = {Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing which samples are best to annotate in order to maximize the performance of the task model. While frameworks for active learning have been widely explored in the context of classification of natural images, they have been only sparsely used in medical image segmentation. The challenge resides in obtaining an uncertainty measure that reveals the best candidate data for annotation. This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation that exploits the uncertainty information offered by data transformations. Our method applies cross-augmentation consistency during training and inference to both improve model learning in a semi-supervised fashion and identify the most relevant unlabeled samples to annotate next. In addition, our consistency loss uses a modified version of the JSD to further improve model performance. By relying on data transformations rather than on external modules or simple heuristics typically used in uncertainty-based strategies, TAAL emerges as a simple, yet powerful task-agnostic semi-supervised active learning approach applicable to the medical domain. Our results on a publicly-available dataset of cardiac images show that TAAL outperforms existing baseline methods in both fully-supervised and semi-supervised settings. Our implementation is publicly available on https://github.com/melinphd/TAAL.},
archivePrefix = {arXiv},
arxivId = {2301.06624},
author = {Gaillochet, M{\'{e}}lanie and Desrosiers, Christian and Lombaert, Herv{\'{e}}},
doi = {10.1007/978-3-031-17027-0_5},
eprint = {2301.06624},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/MICCAI-DALI-Gaillichet-TAAL-ActiveLearning-2022.pdf:pdf},
isbn = {9783031170263},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {43--53},
title = {{TAAL: Test-Time Augmentation for Active Learning in Medical Image Segmentation}},
volume = {13567 LNCS},
year = {2022}
}
@article{Sreenivasaiah,
author = {Sreenivasaiah, Deepthi and Otterbach, Johannes and Wollmann, Thomas and Gmbh, Merantix Labs},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/Sreenivasaiah_MEAL_Manifold_Embedding-Based_Active_Learning_ICCVW_2021_paper.pdf:pdf},
pages = {1029--1037},
title = {{MEAL : Manifold Embedding-based Active Learning}}
}
@article{Aklilu2022,
abstract = {Annotating medical images for the purposes of training computer vision models is an extremely laborious task that takes time and resources away from expert clinicians. Active learning (AL) is a machine learning paradigm that mitigates this problem by deliberately proposing data points that should be labeled in order to maximize model performance. We propose a novel AL algorithm for segmentation, ALGES, that utilizes gradient embeddings to effectively select laparoscopic images to be labeled by some external oracle while reducing annotation effort. Given any unlabeled image, our algorithm treats predicted segmentations as truth and computes gradients with respect to the model parameters of the last layer in a segmentation network. The norms of these per-pixel gradient vectors correspond to the magnitude of the induced change in model parameters and contain rich information about the model's predictive uncertainty. Our algorithm then computes gradients embeddings in two ways, and we employ a center-finding algorithm with these embeddings to procure representative and diverse batches in each round of AL. An advantage of our approach is extensibility to any model architecture and differentiable loss scheme for semantic segmentation. We apply our approach to a public data set of laparoscopic cholecystectomy images and show that it outperforms current AL algorithms in selecting the most informative data points for improving the segmentation model. Our code is available at https://github.com/josaklil-ai/surg-active-learning.},
author = {Aklilu, Josiah and Yeung, Serena},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/aklilu22a.pdf:pdf},
issn = {26403498},
journal = {Proceedings of Machine Learning Research},
pages = {892--911},
title = {{ALGES: Active Learning with Gradient Embeddings for Semantic Segmentation of Laparoscopic Surgical Images}},
volume = {182},
year = {2022}
}
@article{Muller2022,
abstract = {In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful prediction capabilities and achieved similar results as clinicians. However, recent studies revealed that the evaluation in image segmentation studies lacks reliable model performance assessment and showed statistical bias by incorrect metric implementation or usage. Thus, this work provides an overview and interpretation guide on the following metrics for medical image segmentation evaluation in binary as well as multi-class problems: Dice similarity coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's Kappa, and Hausdorff distance. Furthermore, common issues like class imbalance and statistical as well as interpretation biases in evaluation are discussed. As a summary, we propose a guideline for standardized medical image segmentation evaluation to improve evaluation quality, reproducibility, and comparability in the research field.},
archivePrefix = {arXiv},
arxivId = {2202.05273},
author = {M{\"{u}}ller, Dominik and Soto-Rey, I{\~{n}}aki and Kramer, Frank},
doi = {10.1186/s13104-022-06096-y},
eprint = {2202.05273},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/13104_2022_Article_6096.pdf:pdf},
issn = {17560500},
journal = {BMC Research Notes},
keywords = {Biomedical image segmentation; Semantic segmentati,Evaluation,Guideline,Performance assessment,Reproducibility},
number = {1},
pages = {1--8},
pmid = {35725483},
publisher = {BioMed Central},
title = {{Towards a guideline for evaluation metrics in medical image segmentation}},
url = {https://doi.org/10.1186/s13104-022-06096-y},
volume = {15},
year = {2022}
}
@article{Karakida2020,
abstract = {Natural Gradient Descent (NGD) helps to accelerate the convergence of gradient descent dynamics, but it requires approximations in large-scale deep neural networks because of its high computational cost. Empirical studies have confirmed that some NGD methods with approximate Fisher information converge sufficiently fast in practice. Nevertheless, it remains unclear from the theoretical perspective why and under what conditions such heuristic approximations work well. In this work, we reveal that, under specific conditions, NGD with approximate Fisher information achieves the same fast convergence to global minima as exact NGD. We consider deep neural networks in the infinite-width limit, and analyze the asymptotic training dynamics of NGD in function space via the neural tangent kernel. In the function space, the training dynamics with the approximate Fisher information are identical to those with the exact Fisher information, and they converge quickly. The fast convergence holds in layer-wise approximations; for instance, in block diagonal approximation where each block corresponds to a layer as well as in block tridiagonal and K-FAC approximations. We also find that a unit-wise approximation achieves the same fast convergence under some assumptions. All of these different approximations have an isotropic gradient in the function space, and this plays a fundamental role in achieving the same convergence properties in training. Thus, the current study gives a novel and unified theoretical foundation with which to understand NGD methods in deep learning.},
archivePrefix = {arXiv},
arxivId = {2010.00879},
author = {Karakida, Ryo and Osawa, Kazuki},
doi = {10.1088/1742-5468/ac3ae3},
eprint = {2010.00879},
file = {:P\:/cloud_data/Projects/BIOQIC/16_Publications/ALUNet/paper/NeurIPS-2020-understanding-approximate-fisher-information-for-fast-convergence-of-natural-gradient-descent-in-wide-neural-networks-Paper.pdf:pdf},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {NeurIPS},
title = {{Understanding approximate fisher information for fast convergence of natural gradient descent in wide neural networks}},
volume = {2020-Decem},
year = {2020}
}
@article{Liu2021,
abstract = {The existing active learning methods select the samples by evaluating the sample's uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria. In this paper, we propose the Influence Selection for Active Learning(ISAL) which selects the unlabeled samples that can provide the most positive influence on model performance. To obtain the influence of the unlabeled sample in the active learning scenario, we design the Untrained Unlabeled sample Influence Calculation(UUIC) to estimate the unlabeled sample's expected gradient with which we calculate its influence. To prove the effectiveness of UUIC, we provide both theoretical and experimental analyses. Since the UUIC just depends on the model gradients, which can be obtained easily from any neural network, our active learning algorithm is task-agnostic and model-agnostic. ISAL achieves state-of-the-art performance in different active learning settings for different tasks with different datasets. Compared with previous methods, our method decreases the annotation cost at least by 12%, 13% and 16% on CIFAR10, VOC2012 and COCO, respectively.},
archivePrefix = {arXiv},
arxivId = {2108.09331},
author = {Liu, Zhuoming and Ding, Hao and Zhong, Huaping and Li, Weijia and Dai, Jifeng and He, Conghui},
doi = {10.1109/ICCV48922.2021.00914},
eprint = {2108.09331},
file = {:P\:/cloud_data/Projects/Paper/Liu_Influence_Selection_for_Active_Learning_ICCV_2021_paper.pdf:pdf},
isbn = {9781665428125},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {9254--9263},
title = {{Influence Selection for Active Learning}},
year = {2021}
}
@article{Tharwat2023,
abstract = {Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution, whereby a few, high-quality data points are queried by searching for the most informative and representative points within the instance space. This strategy ensures high generalizability across the space and improves classification performance on data we have never seen before. In this paper, we provide a survey of recent studies on active learning in the context of classification. This survey starts with an introduction to the theoretical background of the AL technique, AL scenarios, AL components supported with visual explanations, and illustrative examples to explain how AL simply works and the benefits of using AL. In addition to an overview of the query strategies for the classification scenarios, this survey provides a high-level summary to explain various practical challenges with AL in real-world settings; it also explains how AL can be combined with various research areas. Finally, the most commonly used AL software packages and experimental evaluation metrics with AL are also discussed.},
author = {Tharwat, Alaa and Schenck, Wolfram},
doi = {10.3390/math11040820},
file = {:P\:/cloud_data/Projects/Paper/mathematics-11-00820.pdf:pdf},
issn = {22277390},
journal = {Mathematics},
keywords = {active learning,query strategy,semi-supervised learning,supervised learning},
number = {4},
title = {{A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions}},
volume = {11},
year = {2023}
}
@article{Liu2023,
abstract = {Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? This is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public Medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. The code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://github.com/MedICL-VU/COLosSAL.},
archivePrefix = {arXiv},
arxivId = {2307.12004},
author = {Liu, Han and Li, Hao and Yao, Xing and Fan, Yubo and Hu, Dewei and Dawant, Benoit and Nath, Vishwesh and Xu, Zhoubing and Oguz, Ipek},
eprint = {2307.12004},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu et al. - 2023 - COLosSAL A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation.pdf:pdf},
number = {Dl},
title = {{COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation}},
url = {http://arxiv.org/abs/2307.12004},
year = {2023}
}
@article{Sohn2020,
abstract = {Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 – just 4 labels per class. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. The code is available at https://github.com/google-research/fixmatch.},
archivePrefix = {arXiv},
arxivId = {2001.07685},
author = {Sohn, Kihyuk and Berthelot, David and Li, Chun Liang and Zhang, Zizhao and Carlini, Nicholas and Cubuk, Ekin D. and Kurakin, Alex and Zhang, Han and Raffel, Colin},
eprint = {2001.07685},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sohn et al. - 2020 - SEMI-SUPERVISED FixMatch.pdf:pdf},
journal = {arXiv},
number = {NeurIPS},
title = {{SEMI-SUPERVISED : FixMatch}},
year = {2020}
}
@article{Gonzalez2023,
abstract = {As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net—widely regarded as the best-performing segmenter for multiple medical applications—and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark.},
author = {Gonz{\'{a}}lez, Camila and Ranem, Amin and {Pinto dos Santos}, Daniel and Othman, Ahmed and Mukhopadhyay, Anirban},
doi = {10.1038/s41598-023-34484-2},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gonz{\'{a}}lez et al. - 2023 - Lifelong nnU-Net a framework for standardized medical continual learning.pdf:pdf},
isbn = {0123456789},
issn = {20452322},
journal = {Scientific Reports},
number = {1},
pages = {1--13},
pmid = {37296233},
publisher = {Nature Publishing Group UK},
title = {{Lifelong nnU-Net: a framework for standardized medical continual learning}},
url = {https://doi.org/10.1038/s41598-023-34484-2},
volume = {13},
year = {2023}
}
@article{Mittal2019,
abstract = {Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples. Recently, deep active learning has shown success on various tasks. However, the conventional evaluation scheme used for deep active learning is below par. Current methods disregard some apparent parallel work in the closely related fields. Active learning methods are quite sensitive w.r.t. changes in the training procedure like data augmentation. They improve by a large-margin when integrated with semi-supervised learning, but barely perform better than the random baseline. We re-implement various latest active learning approaches for image classification and evaluate them under more realistic settings. We further validate our findings for semantic segmentation. Based on our observations, we realistically assess the current state of the field and propose a more suitable evaluation protocol.},
archivePrefix = {arXiv},
arxivId = {1912.05361},
author = {Mittal, Sudhanshu and Tatarchenko, Maxim and {\c{C}}i{\c{c}}ek, {\"{O}}zg{\"{u}}n and Brox, Thomas},
eprint = {1912.05361},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mittal et al. - 2019 - Parting with Illusions about Deep Active Learning.pdf:pdf},
number = {3},
title = {{Parting with Illusions about Deep Active Learning}},
url = {http://arxiv.org/abs/1912.05361},
year = {2019}
}
@article{Chan2021,
abstract = {Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and semisupervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-training, that enable superior utilization of unlabeled data which led to a significant reduction in required labeling in the standard machine learning benchmarks. A natural question is whether these paradigms can be unified to obtain superior results. To this aim, this paper provides a novel algorithmic framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. We conduct extensive experiments with our framework on CIFAR10 and CIFAR100 datasets. These experiments enable us to isolate and assess the benefits of individual components which are evaluated using state-of-the-art methods (e.g. Core-Set, VAAL, simCLR, FixMatch). Our experiments reveal two key insights: (i) Selfsupervised pre-training significantly improves semi-supervised learning, especially in the few-label regime, (ii) The benefit of active learning is undermined and subsumed by S4L techniques. Specifically, we fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.},
archivePrefix = {arXiv},
arxivId = {2011.08121},
author = {Chan, Yao Chun and Li, Mingchen and Oymak, Samet},
doi = {10.1109/ICASSP39728.2021.9414665},
eprint = {2011.08121},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chan, Li, Oymak - 2021 - On the marginal benefit of active learning Does self-supervision eat its cake.pdf:pdf},
issn = {15206149},
journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings},
keywords = {Active learning,Contrastive learning,Self-supervision,Semi-supervised learning},
pages = {3455--3459},
title = {{On the marginal benefit of active learning: Does self-supervision eat its cake?}},
volume = {2021-June},
year = {2021}
}
@article{Hacohen2022,
abstract = {Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical examples are best queried when the budget is low, while unrepresentative examples are best queried when the budget is large. Combined evidence shows that a similar phenomenon occurs in common classification models. Accordingly, we propose TypiClust - a deep active learning strategy suited for low budgets. In a comparative empirical investigation of supervised learning, using a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime. Using TypiClust in the semi-supervised framework, performance gets an even more significant boost. In particular, state-of-the-art semi-supervised methods trained on CIFAR-10 with 10 labeled examples selected by TypiClust, reach 93.2% accuracy - an improvement of 39.4% over random selection. Code is available at https://github.com/avihu111/TypiClust.},
archivePrefix = {arXiv},
arxivId = {2202.02794},
author = {Hacohen, Guy and Dekel, Avihu and Weinshall, Daphna},
eprint = {2202.02794},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hacohen, Dekel, Weinshall - 2022 - Active Learning on a Budget Opposite Strategies Suit High and Low Budgets.pdf:pdf},
issn = {26403498},
journal = {Proceedings of Machine Learning Research},
pages = {8175--8195},
title = {{Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets}},
volume = {162},
year = {2022}
}
@article{Huang2022,
abstract = {nnU-Net serves as a good baseline for many medical image segmentation challenges in recent years. It works pretty well for fully-supervised segmentation tasks. However, it is less efficient for inference and cannot effectively make full use of unlabeled data, both of which are vital in real clinical scenarios. To this end, we revisit nnU-Net and find the trade-off between efficiency and accuracy in this framework. Based on the default nnU-Net settings, we design a co-training framework consisting of two strategies to generate high-quality pseudo labels and make efficient inference respectively. Specifically, we first design a resource-intensive nnU-Net to iteratively generate high-quality pseudo labels for unlabeled data. Then we train another light-weight 3D nnU-Net using labeled data and selected unlabeled data, with high-quality pseudo labels used for the latter to achieve efficient segmentation. We conduct experiments on the FLARE22 challenge. Our resource-intensive nnU-Net achieves the mean DSC of 0.9064 on 13 abdominal organ segmentation tasks and ranks first on the validation leaderboard. Our light-weight nnU-Net shows the mean DSC of 0.8773 on the validation leaderboard but it makes a better trade-off between accuracy and efficiency. On the test set, it shows the mean DSC of 0.8864, the mean NSD of 0.9465, and the average inference time of 14.59s and wins the championship of the FLARE22 challenge. Our code is publicly available at https://github.com/Ziyan-Huang/FLARE22.},
author = {Huang, Ziyan and Wang, Haoyu and Ye, Jin and Niu, Jingqi and Tu, Can and Yang, Yuncheng and Du, Shiyi and Deng, Zhongying and Gu, Lixu and He, Junjun},
doi = {10.1007/978-3-031-23911-3_16},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Huang et al. - 2022 - Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference.pdf:pdf},
isbn = {9783031239106},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Computational efficiency,Segmentation,Semi-supervised learning},
pages = {178--189},
title = {{Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference}},
volume = {13816 LNCS},
year = {2022}
}
@article{Burmeister2022a,
abstract = {Since labeling medical image data is a costly and labor-intensive process, active learning has gained much popularity in the medical image segmentation domain in recent years. A variety of active learning strategies have been proposed in the literature, but their effectiveness is highly dependent on the dataset and training scenario. To facilitate the comparison of existing strategies and provide a baseline for evaluating novel strategies, we evaluate the performance of several well-known active learning strategies on three datasets from the Medical Segmentation Decathlon. Additionally, we consider a strided sampling strategy specifically tailored to 3D image data. We demonstrate that both random and strided sampling act as strong baselines and discuss the advantages and disadvantages of the studied methods. To allow other researchers to compare their work to our results, we provide an open-source framework for benchmarking active learning strategies on a variety of medical segmentation datasets.},
archivePrefix = {arXiv},
arxivId = {2207.00845},
author = {Burmeister, Josafat-Mattias and Rosas, Marcel Fernandez and Hagemann, Johannes and Kordt, Jonas and Blum, Jasper and Shabo, Simon and Bergner, Benjamin and Lippert, Christoph},
eprint = {2207.00845},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Burmeister et al. - 2022 - Less Is More A Comparison of Active Learning Strategies for 3D Medical Image Segmentation.pdf:pdf},
pages = {1--22},
title = {{Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation}},
url = {http://arxiv.org/abs/2207.00845},
year = {2022}
}
@article{Munjal2022,
abstract = {Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental settings, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously reported results. We also found that under strong regularization, AL methods show marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL evaluations. We believe our findings and recommendations will help advance reproducible research in AL using neural networks.},
archivePrefix = {arXiv},
arxivId = {2002.09564},
author = {Munjal, Prateek and Hayat, Nasir and Hayat, Munawar and Sourati, Jamshid and Khan, Shadab},
doi = {10.1109/CVPR52688.2022.00032},
eprint = {2002.09564},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Munjal et al. - 2022 - Towards Robust and Reproducible Active Learning using Neural Networks.pdf:pdf},
isbn = {9781665469463},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Efficient learning and inferences,Machine learning,Transparency,accountability,fairness,privacy and ethics in vision},
pages = {223--232},
title = {{Towards Robust and Reproducible Active Learning using Neural Networks}},
volume = {2022-June},
year = {2022}
}
@article{Hacohen2023,
abstract = {In Active Learning (AL), a learner actively chooses which unlabeled examples to query for labels from an oracle, under some budget constraints. Different AL query strategies are more suited to different problems and budgets. Therefore, in practice, knowing in advance which AL strategy is most suited for the problem at hand remains an open problem. To tackle this challenge, we propose a practical derivative-based method that dynamically identifies the best strategy for each budget. We provide theoretical analysis of a simplified case to motivate our approach and build intuition. We then introduce a method to dynamically select an AL strategy based on the specific problem and budget. Empirical results showcase the effectiveness of our approach across diverse budgets and computer vision tasks.},
archivePrefix = {arXiv},
arxivId = {2306.03543},
author = {Hacohen, Guy and Weinshall, Daphna},
eprint = {2306.03543},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hacohen, Weinshall - 2023 - How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget.pdf:pdf},
number = {Section 3},
title = {{How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget}},
url = {http://arxiv.org/abs/2306.03543},
year = {2023}
}
@article{Kirsch2022,
abstract = {The mutual information between predictions and model parameters -- also referred to as expected information gain or BALD in machine learning -- measures informativeness. It is a popular acquisition function in Bayesian active learning and Bayesian optimal experiment design. In data subset selection, i.e. active learning and active sampling, several recent works use Fisher information, Hessians, similarity matrices based on the gradients, or simply the gradient lengths to compute the acquisition scores that guide sample selection. Are these different approaches connected, and if so how? In this paper, we revisit the Fisher information and use it to show how several otherwise disparate methods are connected as approximations of information-theoretic quantities.},
archivePrefix = {arXiv},
arxivId = {2208.00549},
author = {Kirsch, Andreas and Gal, Yarin},
eprint = {2208.00549},
file = {:P\:/cloud_data/Projects/Paper/2208.00549.pdf:pdf},
pages = {1--31},
title = {{Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities}},
url = {http://arxiv.org/abs/2208.00549},
year = {2022}
}
@article{Burmeister2022,
abstract = {Since labeling medical image data is a costly and labor-intensive process, active learning has gained much popularity in the medical image segmentation domain in recent years. A variety of active learning strategies have been proposed in the literature, but their effectiveness is highly dependent on the dataset and training scenario. To facilitate the comparison of existing strategies and provide a baseline for evaluating novel strategies, we evaluate the performance of several well-known active learning strategies on three datasets from the Medical Segmentation Decathlon. Additionally, we consider a strided sampling strategy specifically tailored to 3D image data. We demonstrate that both random and strided sampling act as strong baselines and discuss the advantages and disadvantages of the studied methods. To allow other researchers to compare their work to our results, we provide an open-source framework for benchmarking active learning strategies on a variety of medical segmentation datasets.},
archivePrefix = {arXiv},
arxivId = {2207.00845},
author = {Burmeister, Josafat-Mattias and Rosas, Marcel Fernandez and Hagemann, Johannes and Kordt, Jonas and Blum, Jasper and Shabo, Simon and Bergner, Benjamin and Lippert, Christoph},
eprint = {2207.00845},
file = {:P\:/cloud_data/Projects/Paper/paper33.pdf:pdf},
pages = {1--22},
title = {{Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation}},
url = {http://arxiv.org/abs/2207.00845},
year = {2022}
}
@article{Kothawade2021a,
abstract = {Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in realistic scenarios such as imbalance or rare classes, out-of-distribution data in the unlabeled set, and redundancy. In this work, we propose SIMILAR (Submodular Information Measures based actIve LeARning), a unified active learning framework using recently proposed submodular information measures (SIM) as acquisition functions. We argue that SIMILAR not only works in standard active learning but also easily extends to the realistic settings considered above and acts as a one-stop solution for active learning that is scalable to large real-world datasets. Empirically, we show that SIMILAR significantly outperforms existing active learning algorithms by as much as ≈ 5% - 18% in the case of rare classes and ≈ 5% -10% in the case of out-of-distribution data on several image classification tasks like CIFAR-10, MNIST, and ImageNet. SIMILAR is available as a part of the DISTIL toolkit: https://github.com/decile-team/distil.},
archivePrefix = {arXiv},
arxivId = {2107.00717},
author = {Kothawade, Suraj and Beck, Nathan and Killamsetty, Krishnateja and Iyer, Rishabh},
eprint = {2107.00717},
file = {:P\:/cloud_data/Projects/Paper/8087_similar_submodular_information.pdf:pdf},
isbn = {9781713845393},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {c},
pages = {18685--18697},
title = {{SIMILAR: Submodular Information Measures Based Active Learning in Realistic Scenarios}},
volume = {23},
year = {2021}
}
@article{Mittal2023,
abstract = {Active learning automatically selects samples for annotation from a data pool to achieve maximum performance with minimum annotation cost. This is particularly critical for semantic segmentation, where annotations are costly. In this work, we show in the context of semantic segmentation that the data distribution is decisive for the performance of the various active learning objectives proposed in the literature. Particularly, redundancy in the data, as it appears in most driving scenarios and video datasets, plays a large role. We demonstrate that the integration of semi-supervised learning with active learning can improve performance when the two objectives are aligned. Our experimental study shows that current active learning benchmarks for segmentation in driving scenarios are not realistic since they operate on data that is already curated for maximum diversity. Accordingly, we propose a more realistic evaluation scheme in which the value of active learning becomes clearly visible, both by itself and in combination with semi-supervised learning.},
archivePrefix = {arXiv},
arxivId = {2302.04075},
author = {Mittal, Sudhanshu and Niemeijer, Joshua and Sch{\"{a}}fer, J{\"{o}}rg P. and Brox, Thomas},
eprint = {2302.04075},
file = {:P\:/cloud_data/Projects/Paper/2302.04075.pdf:pdf},
title = {{Revisiting Deep Active Learning for Semantic Segmentation}},
url = {http://arxiv.org/abs/2302.04075},
year = {2023}
}
@article{Chitta2018LargeScaleVA,
author = {Chitta, Kashyap and {\'{A}}lvarez, Jos{\'{e}} Manuel and Lesnikowski, Adam},
journal = {ArXiv},
title = {{Large-Scale Visual Active Learning with Deep Probabilistic Ensembles}},
volume = {abs/1811.0},
year = {2018}
}
@inproceedings{Ronneberger2015a,
abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.},
address = {Cham},
author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M and Frangi, Alejandro F},
isbn = {978-3-319-24574-4},
pages = {234--241},
publisher = {Springer International Publishing},
title = {{U-Net: Convolutional Networks for Biomedical Image Segmentation BT  - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015}},
year = {2015}
}
@article{Ren2022,
abstract = {Active learning (AL) attempts to maximize a model's performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due.It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.},
archivePrefix = {arXiv},
arxivId = {2009.00236},
author = {Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Po Yao and Li, Zhihui and Gupta, Brij B. and Chen, Xiaojiang and Wang, Xin},
doi = {10.1145/3472291},
eprint = {2009.00236},
file = {:P\:/cloud_data/Projects/Paper/3472291.pdf:pdf},
issn = {15577341},
journal = {ACM Computing Surveys},
keywords = {Deep learning,active learning,deep active learning},
number = {9},
title = {{A Survey of Deep Active Learning}},
volume = {54},
year = {2022}
}
@article{Isensee2021,
abstract = {Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.},
author = {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A.A. and Petersen, Jens and Maier-Hein, Klaus H.},
doi = {10.1038/s41592-020-01008-z},
file = {:P\:/cloud_data/Projects/Paper/s41592-020-01008-z.pdf:pdf},
issn = {15487105},
journal = {Nature Methods},
number = {2},
pages = {203--211},
pmid = {33288961},
publisher = {Springer US},
title = {{nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation}},
url = {http://dx.doi.org/10.1038/s41592-020-01008-z},
volume = {18},
year = {2021}
}
@article{Kendall2017a,
abstract = {We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making. Our contribution is a practical system which is able to predict pixel-wise class labels with a measure of model uncertainty using Bayesian deep learning. We achieve this by Monte Carlo sampling with dropout at test time to generate a posterior distribution of pixel class labels. In addition, we show that modelling uncertainty improves segmentation performance by 2-3% across a number of datasets and architectures such as SegNet, FCN, Dilation Network and DenseNet.},
archivePrefix = {arXiv},
arxivId = {1511.02680},
author = {Kendall, Alex and Badrinarayanan, Vijay and Cipolla, Roberto},
doi = {10.5244/c.31.57},
eprint = {1511.02680},
file = {:P\:/cloud_data/Projects/Paper/paper057.pdf:pdf},
isbn = {190172560X},
journal = {British Machine Vision Conference 2017, BMVC 2017},
title = {{Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding}},
year = {2017}
}
@article{Shannon1948,
author = {Shannon, C. E.},
doi = {10.1002/j.1538-7305.1948.tb01338.x},
file = {:P\:/cloud_data/Projects/Paper/entropy.pdf:pdf},
issn = {15387305},
journal = {Bell System Technical Journal},
number = {3},
pages = {379--423},
title = {{A Mathematical Theory of Communication}},
volume = {27},
year = {1948}
}
@article{Gaillochet2023,
abstract = {The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data can be tedious and expensive, especially in medical image segmentation. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches during sampling in AL. Exhaustive experiments on medical image segmentation, with an illustration on MRI prostate imaging, show that the benefits of stochastic batches during sample selection are robust to a variety of changes in the training and sampling procedures.},
archivePrefix = {arXiv},
arxivId = {2301.07670},
author = {Gaillochet, M{\'{e}}lanie and Desrosiers, Christian and Lombaert, Herv{\'{e}}},
eprint = {2301.07670},
file = {:P\:/cloud_data/Projects/Paper/2301.07670.pdf:pdf},
title = {{Active learning for medical image segmentation with stochastic batches}},
url = {http://arxiv.org/abs/2301.07670},
year = {2023}
}
@article{Sinha2019a,
abstract = {Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The mini-max game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on CIFAR10/100, Caltech-256, ImageNet, Cityscapes, and BDD100K. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. Our code is available at url{https://github.com/sinhasam/vaal}.},
archivePrefix = {arXiv},
arxivId = {1904.00370},
author = {Sinha, Samrath and Ebrahimi, Sayna and Darrell, Trevor},
doi = {10.1109/ICCV.2019.00607},
eprint = {1904.00370},
file = {:P\:/cloud_data/Projects/Paper/Sinha_Variational_Adversarial_Active_Learning_ICCV_2019_paper.pdf:pdf},
isbn = {9781728148038},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {5971--5980},
title = {{Variational adversarial active learning}},
volume = {2019-Octob},
year = {2019}
}
@article{Wang2017,
abstract = {Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human effort. In this paper, we propose a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing AL methods in two aspects. First, we incorporate deep convolutional neural networks into AL. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high-confidence samples from the unlabeled set for feature learning. Specifically, these high-confidence samples are automatically selected and iteratively assigned pseudolabels. We thus call our framework cost-effective AL (CEAL) standing for the two advantages. Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification data sets, i.e., face recognition on the cross-age celebrity face recognition data set database and object categorization on Caltech-256.},
archivePrefix = {arXiv},
arxivId = {1701.03551},
author = {Wang, Keze and Zhang, Dongyu and Li, Ya and Zhang, Ruimao and Lin, Liang},
doi = {10.1109/TCSVT.2016.2589879},
eprint = {1701.03551},
file = {:P\:/cloud_data/Projects/Paper/1701.03551.pdf:pdf},
issn = {10518215},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
keywords = {Active learning (AL),deep neural nets,image classification,incremental learning},
number = {12},
pages = {2591--2600},
title = {{Cost-Effective Active Learning for Deep Image Classification}},
volume = {27},
year = {2017}
}
@article{Pratapa2011,
abstract = {Microstructural characterization using XRD and EDS elemental mapping on periclase-added aluminum titanate-corundum functionally-graded materials produced by the multiple infiltration method has been performed to observe the formation of Al2(1-x)MgxTi1 + xO5 solid solution in the phase-compositionally changing materials. XRD data and the Rietveld phase analyses showed that the samples contained only aluminum titanate and corundum with depth-varying compositions. Further analysis on the unit cell volume of aluminum titanate supported by EDS elemental mapping image imparted the formation of the solid solution. Study on the decomposition rate of the samples showed that the solid solution decomposes nearly three times slower than the undoped aluminum titanate. {\textcopyright} 2010 Elsevier B.V. All rights reserved.},
author = {Pratapa, Suminar and Umaroh, Khusnul and Weddakarti, Eva},
doi = {10.1016/j.matlet.2010.11.072},
file = {:P\:/cloud_data/Projects/Paper/settles.activelearning.pdf:pdf},
issn = {0167577X},
journal = {Materials Letters},
keywords = {Aluminum titanate,Decomposition rate,Functionally-graded materials,Solid solution},
number = {5},
pages = {854--856},
title = {{Active Learning Literature Survey}},
volume = {65},
year = {2011}
}
@article{Sinha2019,
abstract = {Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The mini-max game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on CIFAR10/100, Caltech-256, ImageNet, Cityscapes, and BDD100K. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. Our code is available at url{https://github.com/sinhasam/vaal}.},
archivePrefix = {arXiv},
arxivId = {1904.00370},
author = {Sinha, Samrath and Ebrahimi, Sayna and Darrell, Trevor},
doi = {10.1109/ICCV.2019.00607},
eprint = {1904.00370},
file = {:P\:/cloud_data/Projects/Paper/1904.00370.pdf:pdf},
isbn = {9781728148038},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {5971--5980},
title = {{Variational adversarial active learning}},
volume = {2019-Octob},
year = {2019}
}
@article{Ore2018,
archivePrefix = {arXiv},
arxivId = {arXiv:1708.00489v4},
author = {Ore, N Etworks A C and Pproach, E T A and Sener, Ozan and Savarese, Silvio},
eprint = {arXiv:1708.00489v4},
file = {:P\:/cloud_data/Projects/Paper/1708.00489.pdf:pdf},
pages = {1--13},
title = {{ACTIVE LEARNING FOR CONVOLUTIONAL NEURAL NETWORKS: A CORE-SET APPROACH}},
year = {2018}
}
@article{Gal2001,
archivePrefix = {arXiv},
arxivId = {arXiv:1703.02910v1},
author = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
eprint = {arXiv:1703.02910v1},
file = {:P\:/cloud_data/Projects/Paper/1703.02910.pdf:pdf},
title = {{Deep Bayesian Active Learning with Image Data}},
year = {2001}
}
@article{Zhdanov2019,
archivePrefix = {arXiv},
arxivId = {arXiv:1901.05954v1},
author = {Zhdanov, Fedor},
eprint = {arXiv:1901.05954v1},
file = {:P\:/cloud_data/Projects/Paper/1901.05954.pdf:pdf},
title = {{Diverse mini-batch Active Learning}},
year = {2019}
}
@article{Antonelli2022,
abstract = {International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.},
archivePrefix = {arXiv},
arxivId = {2106.05735},
author = {Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and Farahani, Keyvan and Kopp-Schneider, Annette and Landman, Bennett A. and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M. and van Ginneken, Bram and Bilello, Michel and Bilic, Patrick and Christ, Patrick F. and Do, Richard K.G. and Gollub, Marc J. and Heckers, Stephan H. and Huisman, Henkjan and Jarnagin, William R. and McHugo, Maureen K. and Napel, Sandy and Pernicka, Jennifer S.Golia and Rhode, Kawal and Tobon-Gomez, Catalina and Vorontsov, Eugene and Meakin, James A. and Ourselin, Sebastien and Wiesenfarth, Manuel and Arbel{\'{a}}ez, Pablo and Bae, Byeonguk and Chen, Sihong and Daza, Laura and Feng, Jianjiang and He, Baochun and Isensee, Fabian and Ji, Yuanfeng and Jia, Fucang and Kim, Ildoo and Maier-Hein, Klaus and Merhof, Dorit and Pai, Akshay and Park, Beomhee and Perslev, Mathias and Rezaiifar, Ramin and Rippel, Oliver and Sarasua, Ignacio and Shen, Wei and Son, Jaemin and Wachinger, Christian and Wang, Liansheng and Wang, Yan and Xia, Yingda and Xu, Daguang and Xu, Zhanwei and Zheng, Yefeng and Simpson, Amber L. and Maier-Hein, Lena and Cardoso, M. Jorge},
doi = {10.1038/s41467-022-30695-9},
eprint = {2106.05735},
file = {:P\:/cloud_data/Projects/Paper/s41467-022-30695-9.pdf:pdf},
issn = {20411723},
journal = {Nature Communications},
number = {1},
pages = {1--13},
pmid = {35840566},
publisher = {Springer US},
title = {{The Medical Segmentation Decathlon}},
volume = {13},
year = {2022}
}
@article{Li2020,
abstract = {Uncertainty estimation is important for interpreting the trustworthiness of machine learning models in many applications. This is especially critical in the data-driven active learning setting where the goal is to achieve a certain accuracy with minimum labeling effort. In such settings, the model learns to select the most informative unlabeled samples for annotation based on its estimated uncertainty. The highly uncertain predictions are assumed to be more informative for improving model performance. In this paper, we explore uncertainty calibration within an active learning framework for medical image segmentation, an area where labels often are scarce. Various uncertainty estimation methods and acquisition strategies (regions and full images) are investigated. We observe that selecting regions to annotate instead of full images leads to more well-calibrated models. Additionally, we experimentally show that annotating regions can cut 50% of pixels that need to be labeled by humans compared to annotating full images.},
archivePrefix = {arXiv},
arxivId = {2007.06364},
author = {Li, Bo and Alstr{\o}m, Tommy Sonne},
eprint = {2007.06364},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Alstr{\o}m - 2020 - On uncertainty estimation in active learning for image segmentation.pdf:pdf},
month = {jul},
title = {{On uncertainty estimation in active learning for image segmentation}},
url = {http://arxiv.org/abs/2007.06364},
year = {2020}
}
@techreport{Gal2016,
abstract = {Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison , Bayesian models offer a mathematically grounded framework to reason about model uncertainty , but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs-extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predic-tive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.},
author = {Gal, Yarin and Uk, Zg201@cam Ac},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gal, Uk - 2016 - Dropout as a Bayesian Approximation Representing Model Uncertainty in Deep Learning Zoubin Ghahramani.pdf:pdf},
title = {{Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Zoubin Ghahramani}},
url = {http://yarin.co.},
year = {2016}
}
@article{Ash2021,
abstract = {There is an increasing need for effective active learning algorithms that are compatible with deep neural networks. This paper motivates and revisits a classic, Fisher-based active selection objective, and proposes BAIT, a practical, tractable, and high-performing algorithm that makes it viable for use with neural models. BAIT draws inspiration from the theoretical analysis of maximum likelihood estimators (MLE) for parametric models. It selects batches of samples by optimizing a bound on the MLE error in terms of the Fisher information, which we show can be implemented efficiently at scale by exploiting linear-algebraic structure especially amenable to execution on modern hardware. Our experiments demonstrate that BAIT outperforms the previous state of the art on both classification and regression problems, and is flexible enough to be used with a variety of model architectures.},
archivePrefix = {arXiv},
arxivId = {2106.09675},
author = {Ash, Jordan T. and Goel, Surbhi and Krishnamurthy, Akshay and Kakade, Sham},
eprint = {2106.09675},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ash - 2021 - Gone Fishing Neural Active Learning with Fisher Embeddings arXiv 2106 . 09675v2 cs . LG 14 Dec 2021.pdf:pdf},
month = {jun},
title = {{Gone Fishing: Neural Active Learning with Fisher Embeddings}},
url = {http://arxiv.org/abs/2106.09675},
year = {2021}
}
@article{Ash2019,
abstract = {We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.},
archivePrefix = {arXiv},
arxivId = {1906.03671},
author = {Ash, Jordan T. and Zhang, Chicheng and Krishnamurthy, Akshay and Langford, John and Agarwal, Alekh},
eprint = {1906.03671},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ash, Langford - 2020 - D EEP B ATCH A CTIVE L EARNING BY D IVERSE , U NCERTAIN G RADIENT L OWER B OUNDS.pdf:pdf},
month = {jun},
title = {{Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds}},
url = {http://arxiv.org/abs/1906.03671},
year = {2019}
}
@article{Shukla2022,
author = {Shukla, Megh},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shukla - 2022 - Bayesian Uncertainty and Expected Gradient Length - Regression Two Sides Of The Same Coin.pdf:pdf},
title = {{Bayesian Uncertainty and Expected Gradient Length - Regression : Two Sides Of The Same Coin ?}},
year = {2022}
}
@article{Sharma2019,
abstract = {Image segmentation is an essential step in biomedical image analysis. In recent years, deep learning models have achieved significant success in segmentation. However, deep learning requires the availability of large annotated data to train these models, which can be challenging in biomedical imaging domain. In this paper, we aim to accomplish biomedical image segmentation with limited labeled data using active learning. We present a deep active learning framework that selects additional data points to be annotated by combining U-Net with an efficient and effective query strategy to capture the most uncertain and representative points. This algorithm decouples the representative part by first finding the core points in the unlabeled pool and then selecting the most uncertain points from the reduced pool, which are different from the labeled pool. In our experiment, only 13% of the dataset was required with active learning to outperform the model trained on the entire 2018 MICCAI Brain Tumor Segmentation (BraTS) dataset. Thus, active learning reduced the amount of labeled data required for image segmentation without a significant loss in the accuracy.},
author = {Sharma, Dhruv and Shanis, Zahil and Reddy, Chandan K. and Gerber, Samuel and Enquobahrie, Andinet},
doi = {10.1007/978-3-030-33391-1_17},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sharma et al. - 2019 - Active learning technique for multimodal brain tumor segmentation using limited labeled images.pdf:pdf},
isbn = {9783030333904},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Active learning,Deep learning,Segmentation},
pages = {148--156},
title = {{Active learning technique for multimodal brain tumor segmentation using limited labeled images}},
volume = {11795 LNCS},
year = {2019}
}
@article{Achille2019,
abstract = {Whatever information a deep neural network has gleaned from training data is encoded in its weights. How this information affects the response of the network to future data remains largely an open question. Indeed, even defining and measuring information entails some subtleties, since a trained network is a deterministic map, so standard information measures can be degenerate. We measure information in a neural network via the optimal trade-off between accuracy of the response and complexity of the weights, measured by their coding length. Depending on the choice of code, the definition can reduce to standard measures such as Shannon Mutual Information and Fisher Information. However, the more general definition allows us to relate information to generalization and invariance, through a novel notion of effective information in the activations of a deep network. We establish a novel relation between the information in the weights and the effective information in the activations, and use this result to show that models with low (information) complexity not only generalize better, but are bound to learn invariant representations of future inputs. These relations hinge not only on the architecture of the model, but also on how it is trained, highlighting the complex inter-dependency between the class of functions implemented by deep neural networks, the loss function used for training them from finite data, and the inductive bias implicit in the optimization.},
archivePrefix = {arXiv},
arxivId = {1905.12213},
author = {Achille, Alessandro and Paolini, Giovanni and Soatto, Stefano},
eprint = {1905.12213},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Achille, Paolini, Soatto - 2019 - Where is the Information in a Deep Neural Network.pdf:pdf},
title = {{Where is the Information in a Deep Neural Network?}},
url = {http://arxiv.org/abs/1905.12213},
year = {2019}
}
@article{Gorriz2017,
abstract = {We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .},
archivePrefix = {arXiv},
arxivId = {1711.09168},
author = {Gorriz, Marc and Carlier, Axel and Faure, Emmanuel and Giro-i-Nieto, Xavier},
eprint = {1711.09168},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gorriz et al. - 2017 - Cost-Effective Active Learning for Melanoma Segmentation.pdf:pdf},
number = {Nips},
title = {{Cost-Effective Active Learning for Melanoma Segmentation}},
url = {http://arxiv.org/abs/1711.09168},
year = {2017}
}
@article{Nath,
author = {Nath, Vishwesh and Yang, Dong and Landman, Bennett A and Xu, Daguang and Roth, Holger R},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nath et al. - Unknown - Diminishing Uncertainty within the Training Pool Active Learning for Biomedical Image Segmentation.pdf:pdf},
title = {{Diminishing Uncertainty within the Training Pool : Active Learning for Biomedical Image Segmentation}}
}
@article{Karakida2020,
archivePrefix = {arXiv},
arxivId = {arXiv:2010.00879v3},
author = {Karakida, Ryo},
eprint = {arXiv:2010.00879v3},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Karakida - 2020 - Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks.pdf:pdf},
number = {NeurIPS},
title = {{Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks}},
year = {2020}
}
@article{Ash2020,
archivePrefix = {arXiv},
arxivId = {arXiv:1906.03671v2},
author = {Ash, Jordan T and Langford, John},
eprint = {arXiv:1906.03671v2},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ash, Langford - 2020 - D EEP B ATCH A CTIVE L EARNING BY D IVERSE , U NCERTAIN G RADIENT L OWER B OUNDS.pdf:pdf},
title = {{D EEP B ATCH A CTIVE L EARNING BY D IVERSE , U NCERTAIN G RADIENT L OWER B OUNDS}},
year = {2020}
}
@article{Saidu2021,
author = {Saidu, Isah Charles and Csat{\'{o}}, Lehel},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Saidu, Csat{\'{o}} - 2021 - Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation.pdf:pdf},
isbn = {2348079925},
keywords = {ab-unet,active learning,bayesian,bayesian learning,convolutional networks,stochas-,tic gradient descent},
title = {{Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation}},
year = {2021}
}
@article{Yang2017,
abstract = {Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc.), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.},
archivePrefix = {arXiv},
arxivId = {1706.04737},
author = {Yang, Lin and Zhang, Yizhe and Chen, Jianxu and Zhang, Siyuan and Chen, Danny Z.},
doi = {10.1007/978-3-319-66179-7_46},
eprint = {1706.04737},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yang et al. - 2017 - Suggestive annotation A deep active learning framework for biomedical image segmentation.pdf:pdf},
isbn = {9783319661780},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
number = {1},
pages = {399--407},
title = {{Suggestive annotation: A deep active learning framework for biomedical image segmentation}},
volume = {10435 LNCS},
year = {2017}
}
@article{Al2019,
author = {Al, Fi-based},
doi = {10.1007/978-3-030-00889-5},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Al - 2019 - Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation.pdf:pdf},
isbn = {9783030008895},
number = {2018},
title = {{Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation}},
year = {2019}
}
@article{Kirkpatrick2017,
abstract = {The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.},
archivePrefix = {arXiv},
arxivId = {1612.00796},
author = {Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia},
doi = {10.1073/pnas.1611835114},
eprint = {1612.00796},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kirkpatrick et al. - 2017 - Overcoming catastrophic forgetting in neural networks.pdf:pdf},
issn = {10916490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Artificial intelligence,Continual learning,Deep learning,Stability plasticity,Synaptic consolidation},
number = {13},
pages = {3521--3526},
pmid = {28292907},
title = {{Overcoming catastrophic forgetting in neural networks}},
volume = {114},
year = {2017}
}
@article{Gudovskiy2020,
abstract = {Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) by selecting the most representative data points for annotation. However, currently used methods are ill-equipped to deal with biased data. The main motivation of this paper is to consider a realistic setting for pool-based semi-supervised AL, where the unlabeled collection of train data is biased. We theoretically derive an optimal acquisition function for AL in this setting. It can be formulated as distribution shift minimization between unlabeled train data and weakly-labeled validation dataset. To implement such acquisition function, we propose a low-complexity method for feature density matching using self-supervised Fisher kernel (FK) as well as several novel pseudo-label estimators. Our FK-based method outperforms state-of-the-art methods on MNIST, SVHN, and ImageNet classification while requiring only 1/10th of processing. The conducted experiments show at least 40% drop in labeling efforts for the biased class-imbalanced data compared to existing methods.},
author = {Gudovskiy, Denis and Hodgkinson, Alec and Yamaguchi, Takuya and Tsukizawa, Sotaro},
doi = {10.1109/CVPR42600.2020.00906},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gudovskiy et al. - 2020 - Deep active learning for biased datasets via fisher kernel self-supervision.pdf:pdf},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {9038--9046},
title = {{Deep active learning for biased datasets via fisher kernel self-supervision}},
year = {2020}
}
@article{Hesamian2019,
abstract = {Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.},
author = {Hesamian, Mohammad Hesam and Jia, Wenjing and He, Xiangjian and Kennedy, Paul},
doi = {10.1007/s10278-019-00227-x},
file = {:C\:/Users/foellmer/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hesamian et al. - 2019 - Deep Learning Techniques for Medical Image Segmentation Achievements and Challenges.pdf:pdf},
isbn = {1027801900},
issn = {1618727X},
journal = {Journal of Digital Imaging},
keywords = {CNN,Deep learning,Medical image segmentation,Organ segmentation},
number = {4},
pages = {582--596},
pmid = {31144149},
publisher = {Journal of Digital Imaging},
title = {{Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges}},
volume = {32},
year = {2019}
}
