Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

This is the official implementation of Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors 🔗 by Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park, Juhyeon Shin, Uiwon Hwang and Sungroh Yoon (ICLR 2024 Spotlight, Top-5% of the submissions).
This implementation is based on SAR implementation 🔗.

Environments

You should modify [username] and [env_name] in environment.yaml, then

$ conda env create --file environment.yaml

Baselines

TENT 🔗 (ICLR 2021)
EATA 🔗 (ICML 2022)
SAR 🔗 (ICLR 2023)

Dataset

You can download ImageNet-C from a link ImageNet-C 🔗.

After downloading the dataset, move to the root directory ([data_root]) of datasets.

If you run on ColoredMNIST 🔗 or Waterbirds 🔗, run

$ python pretrain_[dataset_name].py --root_dir [data_root] --dset [dataset_name]

Then datasets are automatically downloaded in your [data_root] directory.
(ColoredMNIST from torchvision 🔗 and ./dataset/ColoredMNIST_dataset.py, Waterbirds from wilds 🔗 package)

Your [data_root] will be as follows:

data_root
├── ImageNet-C
│   ├── brightness
│   ├── contrast
│   └── ...
├── ColoredMNIST
│   ├── ColoredMNIST_model.pickle
│   ├── MNIST
│   ├── train1.pt
│   ├── train2.pt
│   └── test.pt
├── Waterbirds
│   ├── metadata.csv
│   ├── waterbirds_dataset.h5py
│   ├── waterbirds_pretrained_model.pickle
│   ├── 001. Black_footed_Albatross
│   ├── 002. Laysan_Albatross
└── └── ...

If you don't want to pre-train, you can just copy and paste the [dataset_name]_model.pickle from './pretrained/' directory.

Experiment

You can run most of the experiments in our paper by

$chmod +x exp_deyo.sh$ ./exp_deyo.sh

If you want to run on the ImageNet-R or VISDA-2021, you should use main_da.py

You should modify ROOT variable as [data_root] in exp_deyo.sh.

Citation

If our DeYO method or biased test-time adaptation settings are helpful in your research, please consider citing our paper:

@inproceedings{
    lee2024entropy,
    title={Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors},
    author={Jonghyun Lee and Dahuin Jung and Saehyung Lee and Junsung Park and Juhyeon Shin and Uiwon Hwang and Sungroh Yoon},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=9w3iw8wDuE}
}

Acknowledgment

The code is inspired by the Tent 🔗, EATA 🔗, and SAR 🔗.