A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

• URL: http://arxiv.org/abs/2303.15361v1
• Date: Mon, 27 Mar 2023 16:32:21 GMT
• Title: A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
• Authors: Jian Liang and Ran He and Tieniu Tan
• Abstract summary: Test-time adaptation, 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 highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference.
• Score: 143.14128737978342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning methods strive to acquire a robust model during training that can generalize well to test samples, even under distribution shifts. However, these methods often suffer from a performance drop 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 highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference. In this survey, we divide TTA into several distinct categories, namely, test-time (source-free) domain adaptation, test-time batch adaptation, online test-time adaptation, and test-time prior adaptation. For each category, we provide a comprehensive taxonomy of advanced algorithms, followed by a discussion of different learning scenarios. Furthermore, we analyze relevant applications of TTA and discuss open challenges and promising areas for future research. A comprehensive list of TTA methods can be found at \url{https://github.com/tim-learn/awesome-test-time-adaptation}.

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