A curated list of awesome test-time (domain/ batch/ instance/ online/ prior) adaptation resources. Your contributions are always welcome!
- [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.
A list of commonly used datasets in TTA is available in Google Sheets.
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}
}-
...[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]