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Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source data.
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Big self-supervised models are strong semi-supervised learners
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Domain adaptive faster r-cnn for object detection in the wild
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Unsupervised representation learning by predicting image rotations
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Cycada: Cycle-consistent adversarial domain adaptation
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Universal source-free domain adaptation
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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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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
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Universal domain adaptation through self supervision
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Liu Zhuang, John Miller, Moritz Hardt, and Alexei A. Efros · 2020
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Unsupervised domain adaptation without source data by casting a bait
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Exploring simple siamese representation learning
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Domain adaptation with auxiliary target domain-oriented classifier
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Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
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Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, and Trevor Darrell · 2021
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Tent: Fully test-time adaptation by entropy minimization
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Generalized source-free domain adaptation
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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