By Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo.
Lunit, Inc.
This repository contains a pytorch implementation of Style-Agnostic Networks (SagNets) for Domain Generalization. It is also an extension of our method which won the first place in Semi-Supervised Domain Adaptation of Visual Domain Adaptation (VisDA)-2019 Challenge. Details are described in Reducing Domain Gap by Reducing Style Bias, CVPR 2021 (Oral).
If you use this code in your research, please cite:
@inproceedings{nam2021reducing,
title={Reducing Domain Gap by Reducing Style Bias},
author={Nam, Hyeonseob and Lee, HyunJae and Park, Jongchan and Yoon, Wonjun and Yoo, Donggeun},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2021}
}
- PyTorch 1.0.0+
- Python 3.6+
- Cuda 8.0+
Download PACS dataset into ./dataset/pacs
images -> ./dataset/pacs/images/kfold/art_painting/dog/pic_001.jpg, ...
splits -> ./dataset/pacs/splits/art_painting_train_kfold.txt, ...
python train.py --sources Rest --targets [domain] --method sagnet --sagnet --batch-size 32 -g [gpus]
python train.py --sources [domain] --targets Rest --method sagnet --sagnet --batch-size 96 -g [gpus]
Results are saved into ./checkpoint
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