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Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios.
Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 1909
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Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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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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Conditional adversarial domain adaptation
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Using self-supervised learning can improve model robustness and uncertainty
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Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
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Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Tent: Fully test-time adaptation by entropy minimization
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Hypothesis disparity regularized mutual information maximization
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Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Pytorch image models
Ross Wightman · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Learning transferable visual models from natural language supervision
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