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Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data.
Dataset shift in machine learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence, · 2008
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Dan Hendrycks and Thomas Dietterich, · 2019
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Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing, · 2019
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“Moment matching for multi-source domain adaptation,”
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang, · 2019
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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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“An image is worth 16x16 words: Transformers for image recognition at scale,”
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al., · 2020
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“On interaction between augmentations and corruptions in natural corruption robustness,”
Eric Mintun, Alexander Kirillov, and Saining Xie, · 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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“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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“Gradual test-time adaptation by self-training and style transfer,”
Robert A Marsden, Mario Döbler, and Bin Yang, · 2022
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“Note: Robust continual test-time adaptation against temporal correlation,”
Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee, · 2022
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“Parameter-free online test-time adaptation,”
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“Continual test-time domain adaptation,”
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai, · 2022
Later among the works it cites.
“Contrastive test-time adaptation,”
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi, · 2022
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“Towards stable test-time adaptation in dynamic wild world,”
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan, · 2023
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“Robust test-time adaptation in dynamic scenarios,”
Longhui Yuan, Binhui Xie, and Shuang Li, · 2023
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“Robust mean teacher for continual and gradual test-time adaptation,”
Mario Döbler, Robert A Marsden, and Bin Yang, · 2023
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“Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction,”
Robert A Marsden, Mario Döbler, and Bin Yang, · 2024
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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
Cited alongside, same era.
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