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Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift.
Imagenet: A large-scale hierarchical image database
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Domain generalization via invariant feature representation
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Baochen Sun and Kate Saenko · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Samuel Dodge and Lina Karam · 2017
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Automatic differentiation in pytorch
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Statistical learning theory
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Domain generalization via conditional invariant representations
Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Adversarial domain adaptation with domain mixup
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Improve unsupervised domain adaptation with mixup training
Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, and Liu Ren · 2020
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Domain generalization via entropy regularization
Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao · 2020
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Revisiting batch normalization for improving corruption robustness
Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon · 2021
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Swad: Domain generalization by seeking flat minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
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Robustbench: a standardized adversarial robustness benchmark
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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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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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Distributionally robust neural networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 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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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
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Improving robustness using generated data
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Mixnorm: Test-time adaptation through online normalization estimation
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Test-time classifier adjustment module for model-agnostic domain generalization
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Sita: Single image test-time adaptation
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Out-of-distribution generalization via risk extrapolation (rex)
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Learning invariant representations and risks for semi-supervised domain adaptation
Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Kurt Keutzer, Trevor Darrell, and Han Zhao · 2021
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Image segmentation using deep learning: A survey
Shervin Minaee, Yuri Y Boykov, Fatih Porikli, Antonio J Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos · 2021
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Reducing domain gap by reducing style bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Test-time batch statistics calibration for covariate shift
Fuming You, Jingjing Li, and Zhou Zhao · 2021
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Test time robustification of deep models via adaptation and augmentation
Marvin Mengxin Zhang, Sergey Levine, and Chelsea Finn · 2021
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Adaptive risk minimization: A meta-learning approach for tackling group shift
Marvin Mengxin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2021
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Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
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