
@article{salimans_weight_2016,
	title = {Weight {Normalization}: {A} {Simple} {Reparameterization} to {Accelerate} {Training} of {Deep} {Neural} {Networks}},
	shorttitle = {Weight {Normalization}},
	url = {http://arxiv.org/abs/1602.07868},
	abstract = {We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.},
	language = {en},
	urldate = {2019-05-22},
	journal = {arXiv:1602.07868 [cs]},
	author = {Salimans, Tim and Kingma, Diederik P.},
	month = feb,
	year = {2016},
	note = {arXiv: 1602.07868},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning},
	file = {Salimans and Kingma - 2016 - Weight Normalization A Simple Reparameterization .pdf:/Users/mx/Zotero/storage/9GBYMDI4/Salimans and Kingma - 2016 - Weight Normalization A Simple Reparameterization .pdf:application/pdf}
}

@article{rezende_variational_2015,
	title = {Variational {Inference} with {Normalizing} {Flows}},
	url = {http://arxiv.org/abs/1505.05770},
	abstract = {The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efﬁcient inference, focusing on mean-ﬁeld or other simple structured approximations. This restriction has a signiﬁcant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying ﬂexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing ﬂow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing ﬂows to develop categories of ﬁnite and inﬁnitesimal ﬂows and provide a uniﬁed view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1505.05770 [cs, stat]},
	author = {Rezende, Danilo Jimenez and Mohamed, Shakir},
	month = may,
	year = {2015},
	note = {arXiv: 1505.05770},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology, Statistics - Computation, Computer Science - Machine Learning},
	file = {Rezende and Mohamed - 2015 - Variational Inference with Normalizing Flows.pdf:/Users/mx/Zotero/storage/VI2WSKER/Rezende and Mohamed - 2015 - Variational Inference with Normalizing Flows.pdf:application/pdf}
}

@article{he_identity_2016,
	title = {Identity {Mappings} in {Deep} {Residual} {Networks}},
	url = {http://arxiv.org/abs/1603.05027},
	abstract = {Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62\% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/ resnet-1k-layers.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1603.05027 [cs]},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = mar,
	year = {2016},
	note = {arXiv: 1603.05027},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {He et al. - 2016 - Identity Mappings in Deep Residual Networks.pdf:/Users/mx/Zotero/storage/JYWYICZZ/He et al. - 2016 - Identity Mappings in Deep Residual Networks.pdf:application/pdf}
}

@article{salimans_pixelcnn++:_2017,
	title = {{PixelCNN}++: {Improving} the {PixelCNN} with {Discretized} {Logistic} {Mixture} {Likelihood} and {Other} {Modifications}},
	shorttitle = {{PixelCNN}++},
	url = {http://arxiv.org/abs/1701.05517},
	abstract = {PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modiﬁcations to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we ﬁnd to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efﬁciently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modiﬁcations.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1701.05517 [cs, stat]},
	author = {Salimans, Tim and Karpathy, Andrej and Chen, Xi and Kingma, Diederik P.},
	month = jan,
	year = {2017},
	note = {arXiv: 1701.05517},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {Salimans et al. - 2017 - PixelCNN++ Improving the PixelCNN with Discretize.pdf:/Users/mx/Zotero/storage/PJBPYH2W/Salimans et al. - 2017 - PixelCNN++ Improving the PixelCNN with Discretize.pdf:application/pdf}
}


@article{atanov_semi-conditional_2019,
	title = {Semi-{Conditional} {Normalizing} {Flows} for {Semi}-{Supervised} {Learning}},
	url = {http://arxiv.org/abs/1905.00505},
	abstract = {This paper proposes a semi-conditional normalizing ﬂow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efﬁciently compute a value and gradients of the marginal likelihood for unlabeled objects. The conditional part of the model is based on a proposed conditional coupling layer. We demonstrate a performance of the model for semi-supervised classiﬁcation problem on different datasets. The model outperforms the baseline approach based on variational autoencoders on MNIST dataset.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1905.00505 [cs, stat]},
	author = {Atanov, Andrei and Volokhova, Alexandra and Ashukha, Arsenii and Sosnovik, Ivan and Vetrov, Dmitry},
	month = may,
	year = {2019},
	note = {arXiv: 1905.00505},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {Atanov et al. - 2019 - Semi-Conditional Normalizing Flows for Semi-Superv.pdf:/Users/mx/Zotero/storage/N5XBMB2A/Atanov et al. - 2019 - Semi-Conditional Normalizing Flows for Semi-Superv.pdf:application/pdf}
}

@article{jacobsen_i-revnet:_2018,
	title = {i-{RevNet}: {Deep} {Invertible} {Networks}},
	shorttitle = {i-{RevNet}},
	url = {http://arxiv.org/abs/1802.07088},
	abstract = {It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difﬁculty of recovering images from their hidden representations, in most commonly used network architectures. In this paper we show via a one-to-one mapping that this loss of information is not a necessary condition to learn representations that generalize well on complicated problems, such as ImageNet. Via a cascade of homeomorphic layers, we build the i-RevNet, a network that can be fully inverted up to the ﬁnal projection onto the classes, i.e. no information is discarded. Building an invertible architecture is difﬁcult, for one, because the local inversion is ill-conditioned, we overcome this by providing an explicit inverse. An analysis of i-RevNets learned representations suggests an alternative explanation for the success of deep networks by a progressive contraction and linear separation with depth. To shed light on the nature of the model learned by the i-RevNet we reconstruct linear interpolations between natural image representations.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1802.07088 [cs, stat]},
	author = {Jacobsen, Jörn-Henrik and Smeulders, Arnold and Oyallon, Edouard},
	month = feb,
	year = {2018},
	note = {arXiv: 1802.07088},
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Jacobsen et al. - 2018 - i-RevNet Deep Invertible Networks.pdf:/Users/mx/Zotero/storage/2MBDDEKD/Jacobsen et al. - 2018 - i-RevNet Deep Invertible Networks.pdf:application/pdf}
}

@article{jacobsen_excessive_2018,
	title = {Excessive {Invariance} {Causes} {Adversarial} {Vulnerability}},
	url = {http://arxiv.org/abs/1811.00401},
	abstract = {Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from -adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-speciﬁc content of almost any image without changing the hidden activations. We identify an insufﬁciency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an informationtheoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the ﬁrst approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.},
	language = {en},
	urldate = {2019-05-20},
	journal = {arXiv:1811.00401 [cs, stat]},
	author = {Jacobsen, Jörn-Henrik and Behrmann, Jens and Zemel, Richard and Bethge, Matthias},
	month = nov,
	year = {2018},
	note = {arXiv: 1811.00401},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {Jacobsen et al. - 2018 - Excessive Invariance Causes Adversarial Vulnerabil.pdf:/Users/mx/Zotero/storage/D53MELGZ/Jacobsen et al. - 2018 - Excessive Invariance Causes Adversarial Vulnerabil.pdf:application/pdf}
}

@article{siddharth_learning_2017,
	title = {Learning {Disentangled} {Representations} with {Semi}-{Supervised} {Deep} {Generative} {Models}},
	url = {http://arxiv.org/abs/1706.00400},
	abstract = {Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.},
	urldate = {2019-05-19},
	journal = {arXiv:1706.00400 [cs, stat]},
	author = {Siddharth, N. and Paige, Brooks and van de Meent, Jan-Willem and Desmaison, Alban and Goodman, Noah D. and Kohli, Pushmeet and Wood, Frank and Torr, Philip H. S.},
	month = jun,
	year = {2017},
	note = {arXiv: 1706.00400},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {arXiv\:1706.00400 PDF:/Users/mx/Zotero/storage/D3J9NH9I/Siddharth et al. - 2017 - Learning Disentangled Representations with Semi-Su.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/GT68GM65/1706.html:text/html}
}

@article{zhao_learning_2019,
	title = {On {Learning} {Invariant} {Representation} for {Domain} {Adaptation}},
	url = {http://arxiv.org/abs/1901.09453},
	abstract = {Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample (Fig. 1) exhibits {\textbackslash}emph\{conditional shift\}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of {\textbackslash}emph\{any\} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.},
	urldate = {2019-04-29},
	journal = {arXiv:1901.09453 [cs, stat]},
	author = {Zhao, Han and Combes, Remi Tachet des and Zhang, Kun and Gordon, Geoffrey J.},
	month = jan,
	year = {2019},
	note = {arXiv: 1901.09453},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {arXiv\:1901.09453 PDF:/Users/mx/Zotero/storage/58VQBKR4/Zhao et al. - 2019 - On Learning Invariant Representation for Domain Ad.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/6Q89ZWZ2/1901.html:text/html}
}

@article{rajaraman_pre-trained_2018,
	title = {Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images},
	volume = {6},
	issn = {2167-8359},
	url = {https://peerj.com/articles/4568},
	doi = {10.7717/peerj.4568},
	abstract = {Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computeraided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.},
	language = {en},
	urldate = {2019-05-16},
	journal = {PeerJ},
	author = {Rajaraman, Sivaramakrishnan and Antani, Sameer K. and Poostchi, Mahdieh and Silamut, Kamolrat and Hossain, Md. A. and Maude, Richard J. and Jaeger, Stefan and Thoma, George R.},
	month = apr,
	year = {2018},
	pages = {e4568},
	file = {Rajaraman et al. - 2018 - Pre-trained convolutional neural networks as featu.pdf:/Users/mx/Zotero/storage/LBV88SBK/Rajaraman et al. - 2018 - Pre-trained convolutional neural networks as featu.pdf:application/pdf}
}

@article{tran_joint_2018,
	title = {Joint {Pixel} and {Feature}-level {Domain} {Adaptation} in the {Wild}},
	url = {http://arxiv.org/abs/1803.00068},
	abstract = {Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature level, inspired by insights from semi-supervised learning in a domain adversarial neural network, we propose a novel regularization in the form of domain adversarial entropy minimization. Next, we posit that insights from computer vision are more amenable to injection at the pixel level and specifically address the key challenge of adaptation across different semantic levels. In particular, we use 3D geometry and image synthesization based on a generalized appearance flow to preserve identity across higher-level pose transformations, while using an attribute-conditioned CycleGAN to translate a single source into multiple target images that differ in lower-level properties such as lighting. We validate on a novel problem of car recognition in unlabeled surveillance images using labeled images from the web, handling explicitly specified, nameable factors of variation through pixel-level and implicit, unspecified factors through feature-level adaptation. Extensive experiments achieve state-of-the-art results, demonstrating the effectiveness of complementing feature and pixel-level information via our proposed domain adaptation method.},
	urldate = {2018-05-25},
	journal = {arXiv:1803.00068 [cs]},
	author = {Tran, Luan and Sohn, Kihyuk and Yu, Xiang and Liu, Xiaoming and Chandraker, Manmohan},
	month = feb,
	year = {2018},
	note = {arXiv: 1803.00068},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1803.00068 PDF:/Users/mx/Zotero/storage/ZW2Y6HUH/Tran et al. - 2018 - Joint Pixel and Feature-level Domain Adaptation in.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/QIG5BAC4/1803.html:text/html}
}

@article{ganin_domain-adversarial_2015,
	title = {Domain-{Adversarial} {Training} of {Neural} {Networks}},
	url = {http://arxiv.org/abs/1505.07818},
	abstract = {We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.},
	urldate = {2018-05-28},
	journal = {arXiv:1505.07818 [cs, stat]},
	author = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, François and Marchand, Mario and Lempitsky, Victor},
	month = may,
	year = {2015},
	note = {arXiv: 1505.07818},
	keywords = {Computer Science - Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	file = {arXiv\:1505.07818 PDF:/Users/mx/Zotero/storage/LICZL2UL/Ganin et al. - 2015 - Domain-Adversarial Training of Neural Networks.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/UGWEWF9U/1505.html:text/html}
}

@article{louizos_variational_2015,
	title = {The {Variational} {Fair} {Autoencoder}},
	url = {http://arxiv.org/abs/1511.00830},
	abstract = {We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of variation. Any subsequent processing, such as classification, can then be performed on this purged latent representation. To remove any remaining dependencies we incorporate an additional penalty term based on the "Maximum Mean Discrepancy" (MMD) measure. We discuss how these architectures can be efficiently trained on data and show in experiments that this method is more effective than previous work in removing unwanted sources of variation while maintaining informative latent representations.},
	urldate = {2018-05-29},
	journal = {arXiv:1511.00830 [cs, stat]},
	author = {Louizos, Christos and Swersky, Kevin and Li, Yujia and Welling, Max and Zemel, Richard},
	month = nov,
	year = {2015},
	note = {arXiv: 1511.00830},
	keywords = {Computer Science - Learning, Statistics - Machine Learning},
	file = {arXiv\:1511.00830 PDF:/Users/mx/Zotero/storage/NSM2GAL7/Louizos et al. - 2015 - The Variational Fair Autoencoder.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/D58CB8KJ/1511.html:text/html}
}

@article{hoffman_cycada:_2017,
	title = {{CyCADA}: {Cycle}-{Consistent} {Adversarial} {Domain} {Adaptation}},
	shorttitle = {{CyCADA}},
	url = {http://arxiv.org/abs/1711.03213},
	abstract = {Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.},
	urldate = {2018-06-01},
	journal = {arXiv:1711.03213 [cs]},
	author = {Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei A. and Darrell, Trevor},
	month = nov,
	year = {2017},
	note = {arXiv: 1711.03213},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1711.03213 PDF:/Users/mx/Zotero/storage/JLUIARBB/Hoffman et al. - 2017 - CyCADA Cycle-Consistent Adversarial Domain Adapta.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/FW9XFNE7/1711.html:text/html}
}

@article{tzeng_adversarial_2017,
	title = {Adversarial {Discriminative} {Domain} {Adaptation}},
	url = {http://arxiv.org/abs/1702.05464},
	abstract = {Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversarial approaches to unsupervised domain adaptation have recently been introduced, which reduce the difference between the training and test domain distributions and thus improve generalization performance. Prior generative approaches show compelling visualizations, but are not optimal on discriminative tasks and can be limited to smaller shifts. Prior discriminative approaches could handle larger domain shifts, but imposed tied weights on the model and did not exploit a GAN-based loss. We first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and we use this generalized view to better relate the prior approaches. We propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard cross-domain digit classification tasks and a new more difficult cross-modality object classification task.},
	urldate = {2018-07-18},
	journal = {arXiv:1702.05464 [cs]},
	author = {Tzeng, Eric and Hoffman, Judy and Saenko, Kate and Darrell, Trevor},
	month = feb,
	year = {2017},
	note = {arXiv: 1702.05464},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1702.05464 PDF:/Users/mx/Zotero/storage/EBZ6QEJR/Tzeng et al. - 2017 - Adversarial Discriminative Domain Adaptation.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/U6APVE5E/1702.html:text/html}
}

@article{lafarge_domain-adversarial_2017,
	title = {Domain-adversarial neural networks to address the appearance variability of histopathology images},
	volume = {10553},
	url = {http://arxiv.org/abs/1707.06183},
	doi = {10.1007/978-3-319-67558-9},
	abstract = {Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.},
	urldate = {2018-07-20},
	journal = {arXiv:1707.06183 [cs]},
	author = {Lafarge, Maxime W. and Pluim, Josien P. W. and Eppenhof, Koen A. J. and Moeskops, Pim and Veta, Mitko},
	year = {2017},
	note = {arXiv: 1707.06183},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1707.06183 PDF:/Users/mx/Zotero/storage/TRFR6E6L/Lafarge et al. - 2017 - Domain-adversarial neural networks to address the .pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/23YXQRVW/1707.html:text/html}
}

@article{kingma_auto-encoding_2013,
	title = {Auto-{Encoding} {Variational} {Bayes}},
	url = {http://arxiv.org/abs/1312.6114},
	abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
	urldate = {2018-07-23},
	journal = {arXiv:1312.6114 [cs, stat]},
	author = {Kingma, Diederik P. and Welling, Max},
	month = dec,
	year = {2013},
	note = {arXiv: 1312.6114},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {arXiv\:1312.6114 PDF:/Users/mx/Zotero/storage/X57W5JJP/Kingma and Welling - 2013 - Auto-Encoding Variational Bayes.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/TARPPRSY/1312.html:text/html}
}

@article{zamir_taskonomy:_2018,
	title = {Taskonomy: {Disentangling} {Task} {Transfer} {Learning}},
	shorttitle = {Taskonomy},
	url = {http://arxiv.org/abs/1804.08328},
	abstract = {Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity.},
	language = {en},
	urldate = {2018-09-14},
	journal = {arXiv:1804.08328 [cs]},
	author = {Zamir, Amir and Sax, Alexander and Shen, William and Guibas, Leonidas and Malik, Jitendra and Savarese, Silvio},
	month = apr,
	year = {2018},
	note = {arXiv: 1804.08328},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Computer Science - Robotics, Computer Science - Machine Learning},
	file = {Zamir et al. - 2018 - Taskonomy Disentangling Task Transfer Learning.pdf:/Users/mx/Zotero/storage/PYCHY9AG/Zamir et al. - 2018 - Taskonomy Disentangling Task Transfer Learning.pdf:application/pdf}
}

@article{hsu_disentangling_2018,
	title = {Disentangling by {Partitioning}: {A} {Representation} {Learning} {Framework} for {Multimodal} {Sensory} {Data}},
	shorttitle = {Disentangling by {Partitioning}},
	url = {http://arxiv.org/abs/1805.11264},
	abstract = {Multimodal sensory data resembles the form of information perceived by humans for learning, and are easy to obtain in large quantities. Compared to unimodal data, synchronization of concepts between modalities in such data provides supervision for disentangling the underlying explanatory factors of each modality. Previous work leveraging multimodal data has mainly focused on retaining only the modality-invariant factors while discarding the rest. In this paper, we present a partitioned variational autoencoder (PVAE) and several training objectives to learn disentangled representations, which encode not only the shared factors, but also modality-dependent ones, into separate latent variables. Specifically, PVAE integrates a variational inference framework and a multimodal generative model that partitions the explanatory factors and conditions only on the relevant subset of them for generation. We evaluate our model on two parallel speech/image datasets, and demonstrate its ability to learn disentangled representations by qualitatively exploring within-modality and cross-modality conditional generation with semantics and styles specified by examples. For quantitative analysis, we evaluate the classification accuracy of automatically discovered semantic units. Our PVAE can achieve over 99\% accuracy on both modalities.},
	urldate = {2018-10-12},
	journal = {arXiv:1805.11264 [cs, eess, stat]},
	author = {Hsu, Wei-Ning and Glass, James},
	month = may,
	year = {2018},
	note = {arXiv: 1805.11264},
	keywords = {Statistics - Machine Learning, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
	file = {arXiv\:1805.11264 PDF:/Users/mx/Zotero/storage/TF4H9YCB/Hsu and Glass - 2018 - Disentangling by Partitioning A Representation Le.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/URBH8UUN/1805.html:text/html}
}

@article{li_domain_2018,
	title = {Domain {Generalization} via {Conditional} {Invariant} {Representation}},
	url = {http://arxiv.org/abs/1807.08479},
	abstract = {Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions. Let \$X\$ denote the features, and \$Y\$ be the class labels. Existing domain generalization methods address the dataset bias problem by learning a domain-invariant representation \$h(X)\$ that has the same marginal distribution \${\textbackslash}mathbb\{P\}(h(X))\$ across multiple source domains. The functional relationship encoded in \${\textbackslash}mathbb\{P\}(Y{\textbar}X)\$ is usually assumed to be stable across domains such that \${\textbackslash}mathbb\{P\}(Y{\textbar}h(X))\$ is also invariant. However, it is unclear whether this assumption holds in practical problems. In this paper, we consider the general situation where both \${\textbackslash}mathbb\{P\}(X)\$ and \${\textbackslash}mathbb\{P\}(Y{\textbar}X)\$ can change across all domains. We propose to learn a feature representation which has domain-invariant class conditional distributions \${\textbackslash}mathbb\{P\}(h(X){\textbar}Y)\$. With the conditional invariant representation, the invariance of the joint distribution \${\textbackslash}mathbb\{P\}(h(X),Y)\$ can be guaranteed if the class prior \${\textbackslash}mathbb\{P\}(Y)\$ does not change across training and test domains. Extensive experiments on both synthetic and real data demonstrate the effectiveness of the proposed method.},
	urldate = {2018-12-05},
	journal = {arXiv:1807.08479 [cs, stat]},
	author = {Li, Ya and Gong, Mingming and Tian, Xinmei and Liu, Tongliang and Tao, Dacheng},
	month = jul,
	year = {2018},
	note = {arXiv: 1807.08479},
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {arXiv\:1807.08479 PDF:/Users/mx/Zotero/storage/K995UCYI/Li et al. - 2018 - Domain Generalization via Conditional Invariant Re.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/UDFQWVXV/1807.html:text/html}
}

@article{mancini_best_2018,
	title = {Best sources forward: domain generalization through source-specific nets},
	shorttitle = {Best sources forward},
	url = {http://arxiv.org/abs/1806.05810},
	abstract = {A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach.},
	urldate = {2018-12-05},
	journal = {arXiv:1806.05810 [cs, stat]},
	author = {Mancini, Massimiliano and Bulò, Samuel Rota and Caputo, Barbara and Ricci, Elisa},
	month = jun,
	year = {2018},
	note = {arXiv: 1806.05810},
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {arXiv\:1806.05810 PDF:/Users/mx/Zotero/storage/K5Q34TKX/Mancini et al. - 2018 - Best sources forward domain generalization throug.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/3SAXWIIP/1806.html:text/html}
}

@article{muandet_domain_2013,
	title = {Domain {Generalization} via {Invariant} {Feature} {Representation}},
	url = {http://arxiv.org/abs/1301.2115},
	abstract = {This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the dissimilarity across domains, whilst preserving the functional relationship between input and output variables. A learning-theoretic analysis shows that reducing dissimilarity improves the expected generalization ability of classifiers on new domains, motivating the proposed algorithm. Experimental results on synthetic and real-world datasets demonstrate that DICA successfully learns invariant features and improves classifier performance in practice.},
	urldate = {2018-12-11},
	journal = {arXiv:1301.2115 [cs, stat]},
	author = {Muandet, Krikamol and Balduzzi, David and Schölkopf, Bernhard},
	month = jan,
	year = {2013},
	note = {arXiv: 1301.2115},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {arXiv\:1301.2115 PDF:/Users/mx/Zotero/storage/CWYKW3F3/Muandet et al. - 2013 - Domain Generalization via Invariant Feature Repres.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/FP55PPAF/1301.html:text/html}
}

@incollection{blanchard_generalizing_2011,
	title = {Generalizing from {Several} {Related} {Classification} {Tasks} to a {New} {Unlabeled} {Sample}},
	url = {http://papers.nips.cc/paper/4312-generalizing-from-several-related-classification-tasks-to-a-new-unlabeled-sample.pdf},
	urldate = {2018-12-11},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 24},
	publisher = {Curran Associates, Inc.},
	author = {Blanchard, Gilles and Lee, Gyemin and Scott, Clayton},
	editor = {Shawe-Taylor, J. and Zemel, R. S. and Bartlett, P. L. and Pereira, F. and Weinberger, K. Q.},
	year = {2011},
	pages = {2178--2186},
	file = {NIPS Full Text PDF:/Users/mx/Zotero/storage/EPIZXF8W/Blanchard et al. - 2011 - Generalizing from Several Related Classification T.pdf:application/pdf;NIPS Snapshot:/Users/mx/Zotero/storage/XV27G85X/4312-generalizing-from-several-related-classification-tasks-to-a-new-unlabeled-sample.html:text/html}
}

@article{higgins_towards_2018,
	title = {Towards a {Definition} of {Disentangled} {Representations}},
	url = {http://arxiv.org/abs/1812.02230},
	abstract = {How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. However, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. In particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. Similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. By connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. Our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. While this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms.},
	urldate = {2018-12-18},
	journal = {arXiv:1812.02230 [cs, stat]},
	author = {Higgins, Irina and Amos, David and Pfau, David and Racaniere, Sebastien and Matthey, Loic and Rezende, Danilo and Lerchner, Alexander},
	month = dec,
	year = {2018},
	note = {arXiv: 1812.02230},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {arXiv\:1812.02230 PDF:/Users/mx/Zotero/storage/VPFL5NUQ/Higgins et al. - 2018 - Towards a Definition of Disentangled Representatio.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/WBT2WL2A/1812.html:text/html}
}

@article{klys_learning_2018,
	title = {Learning {Latent} {Subspaces} in {Variational} {Autoencoders}},
	url = {http://arxiv.org/abs/1812.06190},
	abstract = {Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of unsupervised learning of features correlated to specific labels in a dataset. We propose a VAE-based generative model which we show is capable of extracting features correlated to binary labels in the data and structuring it in a latent subspace which is easy to interpret. Our model, the Conditional Subspace VAE (CSVAE), uses mutual information minimization to learn a low-dimensional latent subspace associated with each label that can easily be inspected and independently manipulated. We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face and CelebA datasets.},
	urldate = {2018-12-19},
	journal = {arXiv:1812.06190 [cs, stat]},
	author = {Klys, Jack and Snell, Jake and Zemel, Richard},
	month = dec,
	year = {2018},
	note = {arXiv: 1812.06190},
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {arXiv\:1812.06190 PDF:/Users/mx/Zotero/storage/5VPEPQMF/Klys et al. - 2018 - Learning Latent Subspaces in Variational Autoencod.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/BBYEU5V6/1812.html:text/html}
}

@article{motiian_unified_2017,
	title = {Unified {Deep} {Supervised} {Domain} {Adaptation} and {Generalization}},
	url = {http://arxiv.org/abs/1709.10190},
	abstract = {This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high speed of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.},
	urldate = {2019-01-29},
	journal = {arXiv:1709.10190 [cs]},
	author = {Motiian, Saeid and Piccirilli, Marco and Adjeroh, Donald A. and Doretto, Gianfranco},
	month = sep,
	year = {2017},
	note = {arXiv: 1709.10190},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1709.10190 PDF:/Users/mx/Zotero/storage/56R697EI/Motiian et al. - 2017 - Unified Deep Supervised Domain Adaptation and Gene.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/VKVPIZA5/1709.html:text/html}
}

@article{chen_isolating_2018,
	title = {Isolating {Sources} of {Disentanglement} in {Variational} {Autoencoders}},
	url = {http://arxiv.org/abs/1802.04942},
	abstract = {We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our \${\textbackslash}beta\$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art \${\textbackslash}beta\$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.},
	urldate = {2019-01-31},
	journal = {arXiv:1802.04942 [cs, stat]},
	author = {Chen, Ricky T. Q. and Li, Xuechen and Grosse, Roger and Duvenaud, David},
	month = feb,
	year = {2018},
	note = {arXiv: 1802.04942},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {arXiv\:1802.04942 PDF:/Users/mx/Zotero/storage/K7JWC82D/Chen et al. - 2018 - Isolating Sources of Disentanglement in Variationa.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/4TVY6DCY/1802.html:text/html}
}

@article{kingma_semi-supervised_2014,
	title = {Semi-{Supervised} {Learning} with {Deep} {Generative} {Models}},
	url = {http://arxiv.org/abs/1406.5298},
	abstract = {The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.},
	urldate = {2019-01-31},
	journal = {arXiv:1406.5298 [cs, stat]},
	author = {Kingma, Diederik P. and Rezende, Danilo J. and Mohamed, Shakir and Welling, Max},
	month = jun,
	year = {2014},
	note = {arXiv: 1406.5298},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {arXiv\:1406.5298 PDF:/Users/mx/Zotero/storage/NUKGYBTL/Kingma et al. - 2014 - Semi-Supervised Learning with Deep Generative Mode.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/MI85A7LK/1406.html:text/html}
}

@article{cohen_group_nodate,
	title = {Group {Equivariant} {Convolutional} {Networks}},
	abstract = {We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers. G-convolutions increase the expressive capacity of the network without increasing the number of parameters. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reﬂections and rotations. G-CNNs achieve state of the art results on CIFAR10 and rotated MNIST.},
	language = {en},
	author = {Cohen, Taco S and Cohen, T S and Nl, Uva},
	pages = {10},
	file = {Cohen et al. - Group Equivariant Convolutional Networks.pdf:/Users/mx/Zotero/storage/9ZXZVRNS/Cohen et al. - Group Equivariant Convolutional Networks.pdf:application/pdf}
}

@article{yosinski_how_2014,
	title = {How transferable are features in deep neural networks?},
	url = {http://arxiv.org/abs/1411.1792},
	abstract = {Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.},
	urldate = {2019-01-31},
	journal = {arXiv:1411.1792 [cs]},
	author = {Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod},
	month = nov,
	year = {2014},
	note = {arXiv: 1411.1792},
	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning},
	file = {arXiv\:1411.1792 PDF:/Users/mx/Zotero/storage/6C6Z2TND/Yosinski et al. - 2014 - How transferable are features in deep neural netwo.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/FBDIWE56/1411.html:text/html}
}

@article{sirinukunwattana_locality_2016,
	title = {Locality {Sensitive} {Deep} {Learning} for {Detection} and {Classification} of {Nuclei} in {Routine} {Colon} {Cancer} {Histology} {Images}},
	volume = {35},
	issn = {0278-0062, 1558-254X},
	url = {http://ieeexplore.ieee.org/document/7399414/},
	doi = {10.1109/TMI.2016.2525803},
	abstract = {Detection and classiﬁcation of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classiﬁcation of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classiﬁcation do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classiﬁcation of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer beneﬁt to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer.},
	language = {en},
	number = {5},
	urldate = {2019-02-13},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Sirinukunwattana, Korsuk and Raza, Shan E Ahmed and Tsang, Yee-Wah and Snead, David R. J. and Cree, Ian A. and Rajpoot, Nasir M.},
	month = may,
	year = {2016},
	pages = {1196--1206},
	file = {Sirinukunwattana et al. - 2016 - Locality Sensitive Deep Learning for Detection and.pdf:/Users/mx/Zotero/storage/KB9T7QC3/Sirinukunwattana et al. - 2016 - Locality Sensitive Deep Learning for Detection and.pdf:application/pdf}
}

@article{ciompi_importance_2017,
	title = {The importance of stain normalization in colorectal tissue classification with convolutional networks},
	url = {http://arxiv.org/abs/1702.05931},
	abstract = {The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H\&E) stained histopathology images requires an accurate and reproducible classiﬁcation of the main tissue components in the image. In this paper, we propose a system for CRC tissue classiﬁcation based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classiﬁcation of CRC tissue samples in H\&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H\&E images.},
	language = {en},
	urldate = {2019-02-13},
	journal = {arXiv:1702.05931 [cs]},
	author = {Ciompi, Francesco and Geessink, Oscar and Bejnordi, Babak Ehteshami and de Souza, Gabriel Silva and Baidoshvili, Alexi and Litjens, Geert and van Ginneken, Bram and Nagtegaal, Iris and van der Laak, Jeroen},
	month = feb,
	year = {2017},
	note = {arXiv: 1702.05931},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Ciompi et al. - 2017 - The importance of stain normalization in colorecta.pdf:/Users/mx/Zotero/storage/I2PFEAQ5/Ciompi et al. - 2017 - The importance of stain normalization in colorecta.pdf:application/pdf}
}


@article{wang_learning_2019,
	title = {Learning {Robust} {Representations} by {Projecting} {Superficial} {Statistics} {Out}},
	url = {http://arxiv.org/abs/1903.06256},
	abstract = {Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superﬁcial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classiﬁer. Building on previous work on domain generalization, we hope to produce a classiﬁer that will generalize to previously unseen domains, even when domain identiﬁers are not available during training. This setting is challenging because the model may extract many distribution-speciﬁc (superﬁcial) signals together with distribution-agnostic (semantic) signals. To overcome this challenge, we incorporate the gray-level cooccurrence matrix (GLCM) to extract patterns that our prior knowledge suggests are superﬁcial: they are sensitive to texture but unable to capture the gestalt of an image. Then we introduce two techniques for improving our networks’ outof-sample performance. The ﬁrst method is built on the reverse gradient method that pushes our model to learn representations from which the GLCM representation is not predictable. The second method is built on the independence introduced by projecting the model’s representation onto the subspace orthogonal to GLCM representation’s. We test our method on battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.},
	language = {en},
	urldate = {2019-05-22},
	journal = {arXiv:1903.06256 [cs]},
	author = {Wang, Haohan and He, Zexue and Lipton, Zachary C. and Xing, Eric P.},
	month = mar,
	year = {2019},
	note = {arXiv: 1903.06256},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Wang et al. - 2019 - Learning Robust Representations by Projecting Supe.pdf:/Users/mx/Zotero/storage/X8YUFC3U/Wang et al. - 2019 - Learning Robust Representations by Projecting Supe.pdf:application/pdf}
}

@article{maaloe_auxiliary_2016,
	title = {Auxiliary {Deep} {Generative} {Models}},
	url = {http://arxiv.org/abs/1602.05473},
	abstract = {Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets.},
	urldate = {2019-02-27},
	journal = {arXiv:1602.05473 [cs, stat]},
	author = {Maaløe, Lars and Sønderby, Casper Kaae and Sønderby, Søren Kaae and Winther, Ole},
	month = feb,
	year = {2016},
	note = {arXiv: 1602.05473},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {arXiv\:1602.05473 PDF:/Users/mx/Zotero/storage/YDS862W2/Maaløe et al. - 2016 - Auxiliary Deep Generative Models.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/IVGMGZBT/1602.html:text/html}
}

@article{azulay_why_2018,
	title = {Why do deep convolutional networks generalize so poorly to small image transformations?},
	url = {http://arxiv.org/abs/1805.12177},
	abstract = {Deep convolutional network architectures are often assumed to guarantee generalization for small image translations and deformations. In this paper we show that modern CNNs (VGG16, ResNet50, and InceptionResNetV2) can drastically change their output when an image is translated in the image plane by a few pixels, and that this failure of generalization also happens with other realistic small image transformations. Furthermore, the deeper the network the more we see these failures to generalize. We show that these failures are related to the fact that the architecture of modern CNNs ignores the classical sampling theorem so that generalization is not guaranteed. We also show that biases in the statistics of commonly used image datasets makes it unlikely that CNNs will learn to be invariant to these transformations. Taken together our results suggest that the performance of CNNs in object recognition falls far short of the generalization capabilities of humans.},
	urldate = {2019-03-09},
	journal = {arXiv:1805.12177 [cs]},
	author = {Azulay, Aharon and Weiss, Yair},
	month = may,
	year = {2018},
	note = {arXiv: 1805.12177},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1805.12177 PDF:/Users/mx/Zotero/storage/XA8AJXUA/Azulay and Weiss - 2018 - Why do deep convolutional networks generalize so p.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/3CMLVTEQ/1805.html:text/html}
}

@inproceedings{torralba_unbiased_2011,
	title = {Unbiased look at dataset bias},
	abstract = {Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue. 1.},
	booktitle = {in {CVPR}},
	author = {Torralba, Antonio and Efros, Alexei A.},
	year = {2011},
	file = {Citeseer - Full Text PDF:/Users/mx/Zotero/storage/DMWGKYTN/Torralba and Efros - 2011 - Unbiased look at dataset bias.pdf:application/pdf;Citeseer - Snapshot:/Users/mx/Zotero/storage/2NB3LAF2/summary.html:text/html}
}

@article{berg_sylvester_2018,
	title = {Sylvester {Normalizing} {Flows} for {Variational} {Inference}},
	url = {http://arxiv.org/abs/1803.05649},
	abstract = {Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.},
	urldate = {2019-03-11},
	journal = {arXiv:1803.05649 [cs, stat]},
	author = {Berg, Rianne van den and Hasenclever, Leonard and Tomczak, Jakub M. and Welling, Max},
	month = mar,
	year = {2018},
	note = {arXiv: 1803.05649},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology, Computer Science - Machine Learning},
	file = {arXiv\:1803.05649 PDF:/Users/mx/Zotero/storage/C8CZ6966/Berg et al. - 2018 - Sylvester Normalizing Flows for Variational Infere.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/GWVQBDFZ/1803.html:text/html}
}

@article{maaloe_biva:_2019,
	title = {{BIVA}: {A} {Very} {Deep} {Hierarchy} of {Latent} {Variables} for {Generative} {Modeling}},
	shorttitle = {{BIVA}},
	url = {http://arxiv.org/abs/1902.02102},
	abstract = {With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data. In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution. We observe that BIVA, in contrast to recent results, can be used for anomaly detection. We attribute this to the hierarchy of latent variables which is able to extract high-level semantic features. Finally, we extend BIVA to semi-supervised classification tasks and show that it performs comparably to state-of-the-art results by generative adversarial networks.},
	urldate = {2019-03-11},
	journal = {arXiv:1902.02102 [cs, stat]},
	author = {Maaløe, Lars and Fraccaro, Marco and Liévin, Valentin and Winther, Ole},
	month = feb,
	year = {2019},
	note = {arXiv: 1902.02102},
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {arXiv\:1902.02102 PDF:/Users/mx/Zotero/storage/2PDKUS8C/Maaløe et al. - 2019 - BIVA A Very Deep Hierarchy of Latent Variables fo.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/D9XACQSX/1902.html:text/html}
}

@article{higgins_-vae:_2017,
	title = {Beta-{VAE}: {Learning} {Basic} {Visual} {Concepts} {with} {a} {constrained} {variational} {framework}},
	abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artiﬁcial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modiﬁcation of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β {\textgreater} 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also signiﬁcantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.},
	language = {en},
	author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
	year = {2017},
	pages = {22},
	file = {Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:/Users/mx/Zotero/storage/AKZPYXQT/Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:application/pdf}
}

@article{shankar_generalizing_2018,
	title = {Generalizing {Across} {Domains} via {Cross}-{Gradient} {Training}},
	url = {http://arxiv.org/abs/1804.10745},
	abstract = {We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other's objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.},
	urldate = {2019-03-11},
	journal = {arXiv:1804.10745 [cs, stat]},
	author = {Shankar, Shiv and Piratla, Vihari and Chakrabarti, Soumen and Chaudhuri, Siddhartha and Jyothi, Preethi and Sarawagi, Sunita},
	month = apr,
	year = {2018},
	note = {arXiv: 1804.10745},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {arXiv\:1804.10745 PDF:/Users/mx/Zotero/storage/3QTDJRNQ/Shankar et al. - 2018 - Generalizing Across Domains via Cross-Gradient Tra.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/P7R5WFWN/1804.html:text/html}
}

@article{pan_survey_2010,
	title = {A {Survey} on {Transfer} {Learning}},
	volume = {22},
	issn = {1041-4347},
	url = {http://ieeexplore.ieee.org/document/5288526/},
	doi = {10.1109/TKDE.2009.191},
	abstract = {A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classiﬁcation task in one domain of interest, but we only have sufﬁcient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classiﬁcation, regression and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as co-variate shift. We also explore some potential future issues in transfer learning research.},
	language = {en},
	number = {10},
	urldate = {2019-03-13},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Pan, Sinno Jialin and Yang, Qiang},
	month = oct,
	year = {2010},
	pages = {1345--1359},
	file = {Pan and Yang - 2010 - A Survey on Transfer Learning.pdf:/Users/mx/Zotero/storage/5T8PNGLV/Pan and Yang - 2010 - A Survey on Transfer Learning.pdf:application/pdf}
}

@article{ruder_overview_2017,
	title = {An {Overview} of {Multi}-{Task} {Learning} in {Deep} {Neural} {Networks}},
	url = {http://arxiv.org/abs/1706.05098},
	abstract = {Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.},
	urldate = {2019-03-13},
	journal = {arXiv:1706.05098 [cs, stat]},
	author = {Ruder, Sebastian},
	month = jun,
	year = {2017},
	note = {arXiv: 1706.05098},
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {arXiv\:1706.05098 PDF:/Users/mx/Zotero/storage/X6959K9I/Ruder - 2017 - An Overview of Multi-Task Learning in Deep Neural .pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/TNYE5H39/1706.html:text/html}
}

@article{bengio_representation_2012,
	title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}},
	shorttitle = {Representation {Learning}},
	url = {http://arxiv.org/abs/1206.5538},
	abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.},
	urldate = {2019-03-13},
	journal = {arXiv:1206.5538 [cs]},
	author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
	month = jun,
	year = {2012},
	note = {arXiv: 1206.5538},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv\:1206.5538 PDF:/Users/mx/Zotero/storage/ZIIDWDYG/Bengio et al. - 2012 - Representation Learning A Review and New Perspect.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/EGW47TET/1206.html:text/html}
}

@article{bouchacourt_multi-level_nodate,
	title = {Multi-{Level} {Variational} {Autoencoder}: {Learning} {Disentangled} {Representations} from {Grouped} {Observations}},
	abstract = {We would like to learn a representation of the data that reﬂects the semantics behind a speciﬁc grouping of the data, where within a group the samples share a common factor of variation. For example, consider a set of face images grouped by identity. We wish to anchor the semantics of the grouping into a disentangled representation that we can exploit. However, existing deep probabilistic models often assume that the samples are independent and identically distributed, thereby disregard the grouping information. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of grouped data. The ML-VAE separates the latent representation into semantically relevant parts by working both at the group level and the observation level, while retaining efﬁcient test-time inference. We experimentally show that our model (i) learns a semantically meaningful disentanglement, (ii) enables control over the latent representation, and (iii) generalises to unseen groups.},
	language = {en},
	author = {Bouchacourt, Diane and Tomioka, Ryota and Nowozin, Sebastian},
	pages = {8},
	year = {2018},
	file = {Bouchacourt et al. - Multi-Level Variational Autoencoder Learning Dise.pdf:/Users/mx/Zotero/storage/SVLWPL7X/Bouchacourt et al. - Multi-Level Variational Autoencoder Learning Dise.pdf:application/pdf}
}

@article{wang_deep_2018,
	title = {Deep {Visual} {Domain} {Adaptation}: {A} {Survey}},
	shorttitle = {Deep {Visual} {Domain} {Adaptation}},
	url = {http://arxiv.org/abs/1802.03601},
	abstract = {Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaption methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaption, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaption scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaption approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.},
	urldate = {2019-03-13},
	journal = {arXiv:1802.03601 [cs]},
	author = {Wang, Mei and Deng, Weihong},
	month = feb,
	year = {2018},
	note = {arXiv: 1802.03601},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1802.03601 PDF:/Users/mx/Zotero/storage/NLWICS77/Wang and Deng - 2018 - Deep Visual Domain Adaptation A Survey.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/RCSWGAUL/1802.html:text/html}
}

@inproceedings{ghifary_domain_2015,
	address = {Santiago, Chile},
	title = {Domain {Generalization} for {Object} {Recognition} with {Multi}-task {Autoencoders}},
	isbn = {978-1-4673-8391-2},
	url = {http://ieeexplore.ieee.org/document/7410650/},
	doi = {10.1109/ICCV.2015.293},
	abstract = {The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for crossdomain object recognition.},
	language = {en},
	urldate = {2019-03-14},
	booktitle = {2015 {IEEE} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	publisher = {IEEE},
	author = {Ghifary, Muhammad and Kleijn, W. Bastiaan and Zhang, Mengjie and Balduzzi, David},
	month = dec,
	year = {2015},
	pages = {2551--2559},
	file = {Ghifary et al. - 2015 - Domain Generalization for Object Recognition with .pdf:/Users/mx/Zotero/storage/ESEEYH4U/Ghifary et al. - 2015 - Domain Generalization for Object Recognition with .pdf:application/pdf}
}

@article{kather_multi-class_2016,
	title = {Multi-class texture analysis in colorectal cancer histology},
	volume = {6},
	issn = {2045-2322},
	url = {http://www.nature.com/articles/srep27988},
	doi = {10.1038/srep27988},
	language = {en},
	number = {1},
	urldate = {2019-04-01},
	journal = {Scientific Reports},
	author = {Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M. and Schad, Lothar R. and Gaiser, Timo and Marx, Alexander and Zöllner, Frank Gerrit},
	month = sep,
	year = {2016},
	file = {Kather et al. - 2016 - Multi-class texture analysis in colorectal cancer .pdf:/Users/mx/Zotero/storage/P72KPFPA/Kather et al. - 2016 - Multi-class texture analysis in colorectal cancer .pdf:application/pdf}
}

@article{chen_variational_2016,
	title = {Variational {Lossy} {Autoencoder}},
	url = {http://arxiv.org/abs/1611.02731},
	abstract = {Representation learning seeks to expose certain aspects of observed data in a learned representation that’s amenable to downstream tasks like classiﬁcation. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only “autoencodes” data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution p(z) and decoding distribution p(x{\textbar}z), we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks as well as competitive results on CIFAR10.},
	language = {en},
	urldate = {2019-04-04},
	journal = {arXiv:1611.02731 [cs, stat]},
	author = {Chen, Xi and Kingma, Diederik P. and Salimans, Tim and Duan, Yan and Dhariwal, Prafulla and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
	month = nov,
	year = {2016},
	note = {arXiv: 1611.02731},
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	file = {Chen et al. - 2016 - Variational Lossy Autoencoder.pdf:/Users/mx/Zotero/storage/IWBQ77RK/Chen et al. - 2016 - Variational Lossy Autoencoder.pdf:application/pdf}
}

@article{ganin_unsupervised_2014,
	title = {Unsupervised {Domain} {Adaptation} by {Backpropagation}},
	url = {http://arxiv.org/abs/1409.7495},
	abstract = {Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary).},
	language = {en},
	urldate = {2019-04-10},
	journal = {arXiv:1409.7495 [cs, stat]},
	author = {Ganin, Yaroslav and Lempitsky, Victor},
	month = sep,
	year = {2014},
	note = {arXiv: 1409.7495},
	keywords = {Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning},
	file = {Ganin and Lempitsky - 2014 - Unsupervised Domain Adaptation by Backpropagation.pdf:/Users/mx/Zotero/storage/6SUQMIV5/Ganin and Lempitsky - 2014 - Unsupervised Domain Adaptation by Backpropagation.pdf:application/pdf}
}

@article{locatello_challenging_2018,
	title = {Challenging {Common} {Assumptions} in the {Unsupervised} {Learning} of {Disentangled} {Representations}},
	url = {http://arxiv.org/abs/1811.12359},
	abstract = {The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties `encouraged' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.},
	urldate = {2019-04-24},
	journal = {arXiv:1811.12359 [cs, stat]},
	author = {Locatello, Francesco and Bauer, Stefan and Lucic, Mario and Rätsch, Gunnar and Gelly, Sylvain and Schölkopf, Bernhard and Bachem, Olivier},
	month = nov,
	year = {2018},
	note = {arXiv: 1811.12359},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv\:1811.12359 PDF:/Users/mx/Zotero/storage/2WUXFSSW/Locatello et al. - 2018 - Challenging Common Assumptions in the Unsupervised.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/WL8E89AC/1811.html:text/html}
}

@article{dai_diagnosing_2019,
	title = {Diagnosing and {Enhancing} {VAE} {Models}},
	url = {http://arxiv.org/abs/1903.05789},
	abstract = {Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true. We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning. Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture. A shorter version of this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf, 2019). The code for our model is available at https://github.com/daib13/ TwoStageVAE.},
	urldate = {2019-04-24},
	journal = {arXiv:1903.05789 [cs, stat]},
	author = {Dai, Bin and Wipf, David},
	month = mar,
	year = {2019},
	note = {arXiv: 1903.05789},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv\:1903.05789 PDF:/Users/mx/Zotero/storage/KPPZTS9D/Dai and Wipf - 2019 - Diagnosing and Enhancing VAE Models.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/89H9XCUU/1903.html:text/html}
}

@article{li_learning_2017,
	title = {Learning to {Generalize}: {Meta}-{Learning} for {Domain} {Generalization}},
	shorttitle = {Learning to {Generalize}},
	url = {http://arxiv.org/abs/1710.03463},
	abstract = {Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. \{Domain Generalization\} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We propose a novel \{meta-learning\} method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training by synthesizing virtual testing domains within each mini-batch. The meta-optimization objective requires that steps to improve training domain performance should also improve testing domain performance. This meta-learning procedure trains models with good generalization ability to novel domains. We evaluate our method and achieve state of the art results on a recent cross-domain image classification benchmark, as well demonstrating its potential on two classic reinforcement learning tasks.},
	urldate = {2019-04-24},
	journal = {arXiv:1710.03463 [cs]},
	author = {Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy M.},
	month = oct,
	year = {2017},
	note = {arXiv: 1710.03463},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv\:1710.03463 PDF:/Users/mx/Zotero/storage/CUP4JUN9/Li et al. - 2017 - Learning to Generalize Meta-Learning for Domain G.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/7IQ5NL44/1710.html:text/html}
}

@article{carlucci_agnostic_2018,
	title = {Agnostic {Domain} {Generalization}},
	url = {http://arxiv.org/abs/1808.01102},
	abstract = {The ability to generalize across visual domains is crucial for the robustness of visual recognition systems in the wild. Several works have been dedicated to close the gap between a single labeled source domain and a target domain with transductive access to its data. In this paper we focus on the wider domain generalization task involving multiple sources and seamlessly extending to unsupervised domain adaptation when unlabeled target samples are available at training time. We propose a hybrid architecture that we name ADAGE: it gracefully maps different source data towards an agnostic visual domain through pixel-adaptation based on a novel incremental architecture, and closes the remaining domain gap through feature adaptation. Both the adaptive processes are guided by adversarial learning. Extensive experiments show remarkable improvements compared to the state of the art.},
	urldate = {2019-04-24},
	journal = {arXiv:1808.01102 [cs]},
	author = {Carlucci, Fabio M. and Russo, Paolo and Tommasi, Tatiana and Caputo, Barbara},
	month = aug,
	year = {2018},
	note = {arXiv: 1808.01102},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1808.01102 PDF:/Users/mx/Zotero/storage/KVPT2AHC/Carlucci et al. - 2018 - Agnostic Domain Generalization.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/MJXVWJBP/1808.html:text/html}
}

@article{ding_deep_2018,
	title = {Deep {Domain} {Generalization} {With} {Structured} {Low}-{Rank} {Constraint}},
	volume = {27},
	issn = {1057-7149, 1941-0042},
	url = {http://ieeexplore.ieee.org/document/8053784/},
	doi = {10.1109/TIP.2017.2758199},
	abstract = {Domain adaptation nowadays attracts increasing interests in pattern recognition and computer vision ﬁeld, since it is an appealing technique in ﬁghting off weakly labeled or even totally unlabeled target data by leveraging knowledge from external well-learned sources. Conventional domain adaptation assumes that target data are still accessible in the training stage. However, we would always confront such cases in reality that the target data are totally blind in the training stage. This is extremely challenging since we have no prior knowledge of the target. In this paper, we develop a deep domain generalization framework with structured low-rank constraint to facilitate the unseen target domain evaluation by capturing consistent knowledge across multiple related source domains. Speciﬁcally, multiple domain-speciﬁc deep neural networks are built to capture the rich information within multiple sources. Meanwhile, a domaininvariant deep neural network is jointly designed to uncover most consistent and common knowledge across multiple sources so that we can generalize it to unseen target domains in the test stage. Moreover, structured low-rank constraint is exploited to align multiple domain-speciﬁc networks and the domaininvariant one in order to better transfer knowledge from multiple sources to boost the learning problem in unseen target domains. Extensive experiments are conducted on several cross-domain benchmarks and the experimental results show the superiority of our algorithm by comparing it with state-of-the-art domain generalization approaches.},
	language = {en},
	number = {1},
	urldate = {2019-04-24},
	journal = {IEEE Transactions on Image Processing},
	author = {Ding, Zhengming and Fu, Yun},
	month = jan,
	year = {2018},
	pages = {304--313},
	file = {Ding and Fu - 2018 - Deep Domain Generalization With Structured Low-Ran.pdf:/Users/mx/Zotero/storage/SIBW2HAC/Ding and Fu - 2018 - Deep Domain Generalization With Structured Low-Ran.pdf:application/pdf}
}

@article{zemel_learning_nodate,
	title = {Learning {Fair} {Representations}},
	abstract = {We propose a learning algorithm for fair classiﬁcation that achieves both group fairness (the proportion of members in a protected group receiving positive classiﬁcation is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of ﬁnding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classiﬁcation tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can ﬁnd important dimensions of the data for classiﬁcation.},
	language = {en},
	author = {Zemel, Richard},
	pages = {9},
	file = {Zemel - Learning Fair Representations.pdf:/Users/mx/Zotero/storage/PUG7SLJS/Zemel - Learning Fair Representations.pdf:application/pdf}
}

@article{kingma_adam:_2014,
	title = {Adam: {A} {Method} for {Stochastic} {Optimization}},
	shorttitle = {Adam},
	url = {http://arxiv.org/abs/1412.6980},
	abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
	urldate = {2019-04-24},
	journal = {arXiv:1412.6980 [cs]},
	author = {Kingma, Diederik P. and Ba, Jimmy},
	month = dec,
	year = {2014},
	note = {arXiv: 1412.6980},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv\:1412.6980 PDF:/Users/mx/Zotero/storage/VQJH52KF/Kingma and Ba - 2014 - Adam A Method for Stochastic Optimization.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/CU4XHSKL/1412.html:text/html}
}

@article{burgess_understanding_2018,
	title = {Understanding disentangling in beta-{VAE}},
	url = {http://arxiv.org/abs/1804.03599},
	abstract = {We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in \${\textbackslash}beta\$-VAE, as training progresses. From these insights, we propose a modification to the training regime of \${\textbackslash}beta\$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in \${\textbackslash}beta\$-VAE, without the previous trade-off in reconstruction accuracy.},
	urldate = {2019-04-24},
	journal = {arXiv:1804.03599 [cs, stat]},
	author = {Burgess, Christopher P. and Higgins, Irina and Pal, Arka and Matthey, Loic and Watters, Nick and Desjardins, Guillaume and Lerchner, Alexander},
	month = apr,
	year = {2018},
	note = {arXiv: 1804.03599},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv\:1804.03599 PDF:/Users/mx/Zotero/storage/AURND26P/Burgess et al. - 2018 - Understanding disentangling in \$beta\$-VAE.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/FII687T6/1804.html:text/html}
}

@InProceedings{pmlr-v28-zemel13,
  title = 	 {Learning Fair Representations},
  author = 	 {Rich Zemel and Yu Wu and Kevin Swersky and Toni Pitassi and Cynthia Dwork},
  booktitle = 	 {Proceedings of the 30th International Conference on Machine Learning},
  pages = 	 {325--333},
  year = 	 {2013},
  editor = 	 {Sanjoy Dasgupta and David McAllester},
  volume = 	 {28},
  number =       {3},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Atlanta, Georgia, USA},
  month = 	 {17--19 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v28/zemel13.pdf},
  url = 	 {http://proceedings.mlr.press/v28/zemel13.html},
  abstract = 	 {We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a  whole), and individual fairness (similar individuals should be treated similarly).  We formulate fairness as an optimization problem of finding a  good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group.  We show positive results of our algorithm relative to other known techniques, on three datasets.  Moreover, we demonstrate several advantages to our approach.  First, our intermediate representation can be used for other classification tasks (i.e., transfer  learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.}
}

@article{rezende2014stochastic,
  title={Stochastic backpropagation and approximate inference in deep generative models},
  author={Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
  journal={arXiv preprint arXiv:1401.4082},
  year={2014}
}

@article{ioffe_batch_2015,
	title = {Batch {Normalization}: {Accelerating} {Deep} {Network} {Training} by {Reducing} {Internal} {Covariate} {Shift}},
	shorttitle = {Batch {Normalization}},
	url = {http://arxiv.org/abs/1502.03167},
	abstract = {Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classiﬁcation model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a signiﬁcant margin. Using an ensemble of batchnormalized networks, we improve upon the best published result on ImageNet classiﬁcation: reaching 4.9\% top-5 validation error (and 4.8\% test error), exceeding the accuracy of human raters.},
	language = {en},
	urldate = {2019-08-16},
	journal = {arXiv:1502.03167 [cs]},
	author = {Ioffe, Sergey and Szegedy, Christian},
	month = feb,
	year = {2015},
	note = {arXiv: 1502.03167},
	keywords = {Computer Science - Machine Learning},
	file = {Ioffe and Szegedy - 2015 - Batch Normalization Accelerating Deep Network Tra.pdf:/Users/mx/Zotero/storage/78CXKBJG/Ioffe and Szegedy - 2015 - Batch Normalization Accelerating Deep Network Tra.pdf:application/pdf}
}

@article{moyer_invariant_2018,
	title = {Invariant {Representations} without {Adversarial} {Training}},
	url = {http://arxiv.org/abs/1805.09458},
	abstract = {Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance to arbitrary nuisance factors yet remain useful for other tasks is challenging. Existing approaches cast the trade-off between task performance and invariance in an adversarial way, using an iterative minimax optimization. We show that adversarial training is unnecessary and sometimes counter-productive; we instead cast invariant representation learning as a single information-theoretic objective that can be directly optimized. We demonstrate that this approach matches or exceeds performance of state-of-the-art adversarial approaches for learning fair representations and for generative modeling with controllable transformations.},
	urldate = {2019-09-16},
	journal = {arXiv:1805.09458 [cs, stat]},
	author = {Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram},
	month = may,
	year = {2018},
	note = {arXiv: 1805.09458},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv\:1805.09458 PDF:/Users/mx/Zotero/storage/4WPSJPGG/Moyer et al. - 2018 - Invariant Representations without Adversarial Trai.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/LIZX3BLW/1805.html:text/html}
}


@article{he_deep_2015,
	title = {Deep {Residual} {Learning} for {Image} {Recognition}},
	url = {http://arxiv.org/abs/1512.03385},
	abstract = {Deeper neural networks are more difﬁcult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classiﬁcation task. We also present analysis on CIFAR-10 with 100 and 1000 layers.},
	language = {en},
	urldate = {2019-09-20},
	journal = {arXiv:1512.03385 [cs]},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = dec,
	year = {2015},
	note = {arXiv: 1512.03385},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf:/Users/mx/Zotero/storage/TSII4KVU/He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf:application/pdf}
}


@article{khemakhem_variational_2019,
	title = {Variational {Autoencoders} and {Nonlinear} {ICA}: {A} {Unifying} {Framework}},
	shorttitle = {Variational {Autoencoders} and {Nonlinear} {ICA}},
	url = {http://arxiv.org/abs/1907.04809},
	abstract = {The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy, undercomplete or discrete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.},
	urldate = {2019-11-13},
	journal = {arXiv:1907.04809 [cs, stat]},
	author = {Khemakhem, Ilyes and Kingma, Diederik P. and Monti, Ricardo Pio and Hyvärinen, Aapo},
	month = oct,
	year = {2019},
	note = {arXiv: 1907.04809},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/Users/mx/Zotero/storage/PHA8QC22/Khemakhem et al. - 2019 - Variational Autoencoders and Nonlinear ICA A Unif.pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/YLQCJGZ7/1907.html:text/html}
}


@inproceedings{cai_learning_2019,
	address = {Macao, China},
	title = {Learning {Disentangled} {Semantic} {Representation} for {Domain} {Adaptation}},
	isbn = {978-0-9992411-4-1},
	url = {https://www.ijcai.org/proceedings/2019/285},
	doi = {10.24963/ijcai.2019/285},
	abstract = {Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space with entangling domain information and semantic information. Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data. In DSR, we assume the data generation process is controlled by two independent sets of variables, i.e., the semantic latent variables and the domain latent variables. Under the above assumption, we employ a variational auto-encoder to reconstruct the semantic latent variables and domain latent variables behind the data. We further devise a dual adversarial network to disentangle these two sets of reconstructed latent variables. The disentangled semantic latent variables are ﬁnally adapted across the domains. Experimental studies testify that our model yields state-of-the-art performance on several domain adaptation benchmark datasets.},
	language = {en},
	urldate = {2019-11-13},
	booktitle = {Proceedings of the {Twenty}-{Eighth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	author = {Cai, Ruichu and Li, Zijian and Wei, Pengfei and Qiao, Jie and Zhang, Kun and Hao, Zhifeng},
	month = aug,
	year = {2019},
	pages = {2060--2066},
	file = {Cai et al. - 2019 - Learning Disentangled Semantic Representation for .pdf:/Users/mx/Zotero/storage/MVTCGEJK/Cai et al. - 2019 - Learning Disentangled Semantic Representation for .pdf:application/pdf}
}

@incollection{zhao_adversarial_2018,
	title = {Adversarial {Multiple} {Source} {Domain} {Adaptation}},
	url = {http://papers.nips.cc/paper/8075-adversarial-multiple-source-domain-adaptation.pdf},
	urldate = {2019-11-13},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 31},
	publisher = {Curran Associates, Inc.},
	author = {Zhao, Han and Zhang, Shanghang and Wu, Guanhang and Moura, José M. F. and Costeira, Joao P and Gordon, Geoffrey J},
	editor = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman, K. and Cesa-Bianchi, N. and Garnett, R.},
	year = {2018},
	pages = {8559--8570},
	file = {NIPS Full Text PDF:/Users/mx/Zotero/storage/9XVKUR2S/Zhao et al. - 2018 - Adversarial Multiple Source Domain Adaptation.pdf:application/pdf;NIPS Snapshot:/Users/mx/Zotero/storage/YI8XZ8QG/8075-adversarial-multiple-source-domain-adaptation.html:text/html}
}

@article{antoran_disentangling_2019,
	title = {Disentangling and Learning Robust Representations with Natural Clustering},
	url = {http://arxiv.org/abs/1901.09415},
	doi = {10.1109/ICMLA.2019.152},
	abstract = {Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-{VAE}, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.},
	journaltitle = {{arXiv}:1901.09415 [cs, stat]},
	author = {Antoran, Javier and Miguel, Antonio},
	urldate = {2020-01-13},
	date = {2019-11-05},
	year = {2019},
	eprinttype = {arxiv},
	eprint = {1901.09415},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/Users/mx/Zotero/storage/PNMJW66U/Antoran and Miguel - 2019 - Disentangling and Learning Robust Representations .pdf:application/pdf;arXiv.org Snapshot:/Users/mx/Zotero/storage/KP2M4BPB/1901.html:text/html}
}