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Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution.
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 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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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Distilling virtual examples for long-tailed recognition
Yin-Yin He, Jianxin Wu, and Xiu-Shen Wei · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
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Progressive domain expansion network for single domain generalization
Lei Li, Ke Gao, Juan Cao, Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Zhengze Yu, Xiaoya Li, and Boyang Xia · 2021
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Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
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AugMix: A simple data processing method to improve robustness and uncertainty
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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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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
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Improving robustness against common corruptions by covariate shift adaptation
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Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, and Jan Hendrik Metzen · 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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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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MEMO: Test time robustness via adaptation and augmentation
M. Zhang, S. Levine, and C. Finn · 2021
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Long-tailed recognition via weight balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, and Shu Kong · 2022
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Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
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Contrastive test-time adaptation
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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