tim-learn/awesome-test-time-adaptation
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Collection of awesome test-time (domain/batch/instance) adaptation methods
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README.md
OriginalAwesome Test-Time Adaptation 
A curated list of awesome test-time (domain/ batch/ instance/ online/ prior) adaptation resources. Your contributions are always welcome!
Problem

Update
- [Sep 8, 2025] Interested in maintaining this project? We're looking for contributors to help keep it active and growing. Feel free to open an issue or reach out to me via email.
Contents
Datasets
A list of commonly used datasets in TTA is available in Google Sheets.
Citation
If you find our survey and repository useful for your research, please consider citing our paper:
@article{liang2023ttasurvey,
title={A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts},
author={Liang, Jian and He, Ran and Tan, Tieniu},
journal={International Journal Of Computer Vision},
year={2023}
}
Surveys and benchmarks
...[Liang et al., IJCV 2024] A comprehensive survey on test-time adaptation under distribution shifts [PDF] [G-Scholar]...[Liu et al., arXiv 2021] Data-free knowledge transfer: A survey [PDF] [G-Scholar]...[Zhang et al., Neurocomputing 2023] Source-free unsupervised domain adaptation: Current research and future directions [PDF] [G-Scholar]...[Fang et al., Neural Networks 2024] Source-free unsupervised domain adaptation: A survey [PDF] [G-Scholar]...[Li et al., IEEE TPAMI 2024] A comprehensive survey on source-free domain adaptation [PDF] [G-Scholar]...[Wang et al., IJCV 2024] In search of lost online test-time adaptation: A survey [PDF] [G-Scholar]...[Xiao and Snoek, arXiv 2024] Beyond model adaptation at test time: A survey [PDF] [G-Scholar--]...[Yu et al., arXiv 2023] Benchmarking test-time adaptation against distribution shifts in image classification [PDF] [G-Scholar] [CODE]
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