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Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions.
Increasing the robustness of dnns against image corruptions by playing the game of noise
Evgenia Rusak, Lukas Schott, Roland Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2001
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Image quality assessment: From error visibility to structural similarity
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Covariate shift and local learning by distribution matching, 2008
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and N Lawrence · 2008
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2017
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Autodial: Automatic domain alignment layers
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, and Samuel Rota Bulo · 2017
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 2019
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Norman Mu and Justin Gilmer · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel · 2019
Entropy minimization vs. diversity maximization for domain adaptation
Xiaofu Wu, Quan Zhou, Zhen Yang, Chunming Zhao, Longin Jan Latecki, et al · 2020
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Benchmarking the robustness of semantic segmentation models
Christoph Kamann and Carsten Rother · 2020
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Increasing the robustness of semantic segmentation models with painting-by-numbers
Christoph Kamann, Burkhard Güssefeld, Robin Hutmacher, Jan Hendrik Metzen, and Carsten Rother · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, Rahul M V, and R. Venkatesh Babu · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Adversarial examples are a natural consequence of test error in noise
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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Fully test-time adaptation by entropy minimization
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The many faces of robustness: A critical analysis of out-of-distribution generalization, 2020
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
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Negative log likelihood ratio loss for deep neural network classification
Hengshuai Yao, Dong-lai Zhu, Bei Jiang, and Peng Yu · 2020
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Torchvision models, 2020
Torch-Contributors · 2020
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Domain impression: A source data free domain adaptation method
Vinod K. Kurmi, Venkatesh K. Subramanian, and Vinay P. Namboodiri · 2021
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Sofa: Source-data-free feature alignment for unsupervised domain adaptation
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Revisiting batch normalization for improving corruption robustness
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Learning to resize images for computer vision tasks
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