This is the official implementation for "Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition".
Please create and activate the following conda envrionment.
# It may take several minutes for conda to solve the environment
conda create -y -n oftta python=3.9
conda activate oftta
pip install -r requirements.txt Three datasets (UCI-HAR, Opportunity, and UniMiB-SHAR) are utilized in the experiments. The pre-processed outcome can be downloaded from here. The datasets is adopt from GILE. Please save datasets under folder ./data.
Since test-time adaptation needs pre-trained on source domains. We provide our used model in Table 4. You can download the model from here. Please save datasets under folder ./ckpt. If you want to train your model from scratch, you can refer to code for generalizable HAR.
To reproduce the leave-one-out adaptation results in Table 4, you just need:
bash adapt.shYou can get the all results in Table 4.
We support all the TTA algorithms used in the paper. Feel free to adopt them on other types of dataset.
| Title | Venue |
|---|---|
| PL: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks | ICML Workshop 2013 |
| SHOT: Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation | ICML 2020 |
| BN: Improving robustness against common corruptions by covariate shift adaptation | NeurIPS 2020 |
| TENT: Tent: Fully test-time adaptation by entropy minimization | ICLR 2021 |
| T3A: Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization | NeurIPS 2021 |
| TAST: Test-time Adaptation via Self-training with Nearest Neighbor information | ICLR 2023 |
| SAR: Towards Stable Test-time Adaptation in Dynamic Wild World | ICLR 2023 |