@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",
pages =        "891--921",
year =         "1905"}

@book{latexcompanion,
author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
title     = "The \LaTeX\ Companion",
year      = "1993",
publisher = "Addison-Wesley"}

@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Robustness % (2)
@article{Robustness_Imagenet-C,
author = {D. Hendrycks and T. Dietterich.},
title = {Benchmarking neural network robustness to common corruptions and perturbations.},
journal = {Proceedings of the International Conference on Learning Representations},
volume = {}, 
pages = {}, 
year = 2019
}

@article{Robustness_Adversarial,
author = {Ian J. Goodfellow and Jonathon Shlens and Christian Szegedy},
title = {Explaining and Harnessing Adversarial Examples},
journal = {Proceedings of the International Conference on Learning Representations},
volume = {}, 
pages = {}, 
year = 2015
}

% Dataset % (1)
@article{Dataset_Imagenet,
author = {J. Deng and W. Dong and R. Socher and L. Jia Li and K. Li and L. Fei-Fei},
title = {Imagenet: A large-scale hierarchical image database},
journal = {IEEE conference on computer vision and pattern recognition},
volume = {}, 
pages = {248–255}, 
year = 2009
}
@article{Dataset_MNIST,
  title={The mnist database of handwritten digit images for machine learning research},
  author={Deng, Li},
  journal={IEEE Signal Processing Magazine},
  volume={29},
  number={6},
  pages={141--142},
  year={2012},
  publisher={IEEE}
}

% Data Augmentation % (4)
@article{DataAugmentation_survey,
author = {Connor Shorten and Taghi M. Khoshgoftaar},
title = {A survey on Image Data Augmentation for Deep Learning},
journal = {Journal of Big Data},
volume = {6}, 
pages = {60}, 
year = 2019
}

@InProceedings{DataAugmentation_autoaugment,
author = {Cubuk, Ekin D. and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V.},
title = {AutoAugment: Learning Augmentation Strategies From Data},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@inproceedings{DataAugmentation_fastautoaugment,
 author = {Lim, Sungbin and Kim, Ildoo and Kim, Taesup and Kim, Chiheon and Kim, Sungwoong},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Fast AutoAugment},
 volume = {32},
 year = {2019}
}

@article{DataAugmentation_augmix,
title = {AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty},
author = {Dan, Hendrycks and Norman, Mu and Ekin.D, Cubuk and Barret, Zoph and Justin, Gilmer and Balaji, Lakshminarayanan},
author = {Dan Hendrycks, Norman Mu},
journal = {Proceedings of the International Conference on Learning Representations},
volume = {}, 
pages = {}, 
year = 2020
}

% TTA Policy % (4) 여기까지함
@article{TTA-Policy_APAC,
author = {Ikuro Sato and Hiroki Nishimura and Kensuke Yokoi.},
title = {APAC:
Augmented pattern classification with neural networks},
journal = {arXiv preprint arXiv:1505.03229},
volume = {}, 
pages = {}, 
year = 2015
}

@misc{TTA-Policy_GPS,
      title={Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation}, 
      author={Dmitry Molchanov and Alexander Lyzhov and Yuliya Molchanova and Arsenii Ashukha and Dmitry Vetrov},
      year={2020},
      eprint={2002.09103},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@article{TTA-Policy_WhenAndWhy,
author = {Divya Shanmugam and Davis Blalock and Guha Balakrishnan and John Guttag},
title = {When and Why Test-Time Augmentation Works},
journal = {arXiv:2011.11156},
volume = {}, 
pages = {}, 
year = 2020
}

@inproceedings{TTA-Policy_L2T,
 author = {Kim, Ildoo and Kim, Younghoon and Kim, Sungwoong },
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Learning Loss for Test-Time Augmentation},
 volume = {33},
 year = {2020}
}


% TTA-Training % (1)
@InProceedings{TTA-distillation,
author = {Radosavovic, Ilija and Dollár, Piotr and Girshick, Ross and Gkioxari, Georgia and He, Kaiming},
title = {Data Distillation: Towards Omni-Supervised Learning},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

% TTA Applications (4)
@inproceedings{Alexnet,
 author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {ImageNet Classification with Deep Convolutional Neural Networks},
 year = {2012}
}

@InProceedings{GoogleNet,
author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
title = {Going Deeper With Convolutions},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2015}
}

@inproceedings{VGG,
  author    = {Karen Simonyan and
               Andrew Zisserman},
  title     = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
  booktitle = {3rd International Conference on Learning Representations, {ICLR} },
  year      = {2015}

}
@InProceedings{Resnet,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
title = {Deep Residual Learning for Image Recognition},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

@InProceedings{EfficientNet, 
title = {Efficientnet: Rethinking model scaling for convolutional neural networks.}, 
author = {M. Tan and Q. Le.},
booktitle = {International Conference on Machine Learning},
pages = {6105--6114}, 
year = {2019}
}



% TTA Uncertainty (4)



@InProceedings{ActiveLearning_Lossprediction,
author = {Yoo, Donggeun and Kweon, In So},
title = {Learning Loss for Active Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

    % also for med
@article{TTA_Uncertainty_med1,
author = {Guotai Wang and Wenqi Li and Michael Aertsen and Jan Deprest and Sébastien Ourselin and Tom Vercauteren},
title = {Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks},
journal = {Neurocomputing},
year = {2019}
}

    % also for med
@article{TTA_Uncertainty_med2,
author = {Ayhan M.S. and Berens, P.},
title = {Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks},
journal = {Medical
Imaging with Deep Learning},
year = {2018}
}

@article{Uncertainty_estimation,
author = {Yarin Gal},
title = {Uncertainty in deep learning.},
journal = {\em University of Cambridge},
note = {1:3},
year = {2016}
}
@article{TTA_Uncertainty_pitfall,
author = {Arsenii Ashukha and Alexander Lyzhov and Dmitry Molchanov and Dmitry Vetrov},
title = {Pitfalls of in-domain uncertainty estimation and ensembling in deep learning.},
journal = {Proceedings of the International Conference on Learning Representations},
year = {2020}
}


% TTA med biological () 4~5

@inproceedings{TTA_med_1,
author = {Perez, Fábio and Vasconcelos, Cristina and Avila, Sandra and Valle, Eduardo},
journal = { in Proc. International Workshop on Computer-Assisted and Robotic Endoscopy,},
year = {2018},
month = {08},
pages = {pp. 303-311,},
title = {Data Augmentation for Skin Lesion Analysis}
}
@article{TTA_med_2,
author = {Matsunaga, Kazuhisa and Hamada, Akira and Minagawa, Akane and Koga, Hiroshi},
journal = {arXiv preprint arXiv:1703.03108.},
year = {2017},
pages = {},
title = {Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble}
}
@article{TTA_med_3,
author = {Moshkov, N. and Mathe, B. and Kertesz-Farkas and A. et al.},
title = {Test-time augmentation for deep learning-based cell segmentation on microscopy images.},
journal = {Sci Rep},
volume = {10, 5068},
year = {2020}
}


% Entropy Weighting Method 2~3
@article{EWM_1,
author = {Vahab Amiri and Mohsen Rezaei and Nasim Sohrabi},
title = {“Groundwater quality assessment using entropy weighted water
quality index (EWQI) in Lenjanat, Iran},
journal = {Environmental Earth Sciences},
volume = {vol. 72},
year = {2014}
}
@article{EWM_2,
author = {L. Liu and J. Zhou and X. An and Y. Zhang and L. Yang},
title = {“Using fuzzy
theory and information entropy for water quality assessment
in three Gorges region, China,},
journal = {Expert Systems with Applications},
volume = {vol. 37},
year = {2010}
}
@article{EWM_3,
author = {Yuxin Zhu and Dazuo Tian and Feng Yan},
title = {“Effectiveness of Entropy Weight Method in Decision-Making},
journal = {Mathematical Problems in Engineering},
year = {2020}
}
% efficient net, resnet




% etc: sodeep CIFAR100

@InProceedings{Data_Distillation,
author = {Radosavovic, Ilija and Dollár, Piotr and Girshick, Ross and Gkioxari, Georgia and He, Kaiming},
title = {Data Distillation: Towards Omni-Supervised Learning},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

@article{Uncertainty_Ensemble,
author = {Andrey Malinin and Bruno Mlodozeniec and Mark Gales},
title = {ensemble distribution distillation.},
journal = {Proceedings of the International Conference on Learning Representations},
volume = {}, 
pages = {}, 
year = 2020
}
@InProceedings{sodeep,
author = {Engilberge, Martin and Chevallier, Louis and Perez, Patrick and Cord, Matthieu},
title = {SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

