Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
AAAI 2024 (Oral)
## ***(Code coming soon)***
## Background
We focus on the problem of *Test-time Domain Adaptation (TTDA)* or *Few-shot TTDA*. When an unseen target domain is encountered at test-time, a few unlabeled images are sampled to update the model towards that domain. The adapted model is then used for testing the data in that domain.
## Method overview
## :star: Acknowledgement
Our code is built upon the codebase from [MetaDMoE (NeurIPS22)](https://github.com/n3il666/Meta-DMoE)
##
:clipboard: Citation
If you use this code in your research, please consider citing our paper:
```
@inproceedings{wu2024test,
title={Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization},
author={Wu, Yanan and Chi, Zhixiang and Wang, Yang and Plataniotis, Konstantinos N and Feng, Songhe},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024}
}
```
