Computer Science > Machine Learning
arXiv:2303.15361 (cs)
[Submitted on 27 Mar 2023 (v1), last revised 12 Dec 2024 (this version, v2)]
Title:A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
View a PDF of the paper titled A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts, by Jian Liang and Ran He and Tieniu Tan
View PDFHTML (experimental)Abstract:Machine learning methods strive to acquire a robust model during the training process that can effectively generalize to test samples, even in the presence of distribution shifts. However, these methods often suffer from performance degradation due to unknown test distributions. Test-time adaptation (TTA), an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. Recent progress in this paradigm has highlighted the significant benefits of using unlabeled data to train self-adapted models prior to inference. In this survey, we categorize TTA into several distinct groups based on the form of test data, namely, test-time domain adaptation, test-time batch adaptation, and online test-time adaptation. For each category, we provide a comprehensive taxonomy of advanced algorithms and discuss various learning scenarios. Furthermore, we analyze relevant applications of TTA and discuss open challenges and promising areas for future research. For a comprehensive list of TTA methods, kindly refer to \url{this https URL}.
| Comments: | Discussions, comments, and questions are all welcomed in \url{this https URL} |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2303.15361 [cs.LG] |
| (orarXiv:2303.15361v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2303.15361 arXiv-issued DOI via DataCite | |
| Journal reference: | International Journal of Computer Vision (2024) |
| Related DOI: | https://doi.org/10.1007/s11263-024-02181-w DOI(s) linking to related resources |
Submission history
From: Jian Liang [view email][v1] Mon, 27 Mar 2023 16:32:21 UTC (1,021 KB)
[v2] Thu, 12 Dec 2024 09:06:56 UTC (686 KB)
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View a PDF of the paper titled A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts, by Jian Liang and Ran He and Tieniu Tan
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