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https://github.com/MediaBrain-SJTU/DISAM
This repository contains the implementation details for the paper "Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts," accepted at the ICLR 2024.
https://github.com/MediaBrain-SJTU/DISAM
Last synced: about 1 month ago
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This repository contains the implementation details for the paper "Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts," accepted at the ICLR 2024.
- Host: GitHub
- URL: https://github.com/MediaBrain-SJTU/DISAM
- Owner: MediaBrain-SJTU
- Created: 2023-11-14T04:18:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-26T10:58:34.000Z (9 months ago)
- Last Synced: 2024-05-14T00:16:04.522Z (8 months ago)
- Language: Python
- Homepage:
- Size: 10.6 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
This repository contains the implementation details for the paper "Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts," accepted at the International Conference on Learning Representations (ICLR) 2024.
![poster image](./DISAM_poster_online.png)
## Environment Requirements
![Language](https://img.shields.io/badge/language-python-brightgreen)
![Python](https://img.shields.io/badge/Python->=3.9.5-orange)
![PyTorch](https://img.shields.io/badge/PyTorch-=2.0.1-orange)
![NumPy](https://img.shields.io/badge/NumPy->=1.23.5-orange)## Usage
### Dataset ![repo](https://img.shields.io/badge/repo-DomainBed-informational)
You need to download the dataset on your own and specify the dataset path in the `code/configs/default.py` file. Please refer to [Domainbed repo](https://github.com/facebookresearch/DomainBed).
### Algorithm
The core operations of the algorithm are implemented in the `code/algorithms/DISAM.py` file.
### Example Run Command
```bash
bash ./runs/run_trainer.py --algorithm DISAM_Trainer --dataset pacs --test_domain p --lambda_weight 0.1 --rho 0.05 --lr 1e-3 --batch_size 32 --epoch 50
```## Citation
If you find our work useful in your research, please consider citing:
```bibtex
@inproceedings{zhang2024domaininspired,
title={Domain-Inspired Sharpness Aware Minimization Under Domain Shifts},
author={Ruipeng Zhang and Ziqing Fan and Jiangchao Yao and Ya Zhang and Yanfeng Wang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=I4wB3HA3dJ}
}
```## License
![License](https://img.shields.io/badge/license-MIT-yellow)
This project is licensed under the MIT License.