CSAC
Introduction
This repository contains the implementation code for paper:
Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin
arXiv preprint, 2021
[arXiv]
Brief Abstract for the Paper
The existing domain generalization (DG) methods usually exploit the fusion of shared multi-source data for capturing domain invariance and training a generalizable model, which raises a dilemma between the generalization learning with shared multi-source data and the privacy protection of real-world sensitive data.
We introduce a separated domain generalization task with separated source datasets that can only be accessed locally for data privacy protection.
We propose a novel solution called Collaborative Semantic Aggregation and Calibration (CSAC) to enable this challenging task via local semantic acquisition, data-free semantic aggregation, and cross-layer semantic calibration.
Requirements
You may need to build suitable Python environment by installing the following packages (Anaconda is recommended).
- python 3.8
- pytorch 1.8.1 (with cuda 11.3)
- torchvision 0.9.1
- tensorboardx 2.4
- numpy 1.21
- qpsolvers 1.7
Device:
- GPU with VRAM > 11GB (strictly).
- Memory > 8GB.
Data Preparation
We list the adopted datasets in the following.
Datasets | Download link |
---|---|
PACS [1] | https://dali-dl.github.io/project_iccv2017.html |
VLCS [2] | http://www.mediafire.com/file/7yv132lgn1v267r/vlcs.tar.gz/file |
Please note:
- Our dataset split follows previous works like RSC (Code) [3].
- Although these datasets are open-sourced, you may need to have permission to use the datasets under the datasets' license.
- If you're a dataset owner and do not want your dataset to be included here, please get in touch with us via a GitHub issue. Thanks!
Usage
- Prepare the datasets.
- Update root_dir in configs/datasets/dg/pacs.yaml/ and configs/datasets/dg/vlcs.yaml/ with the paths of PACS and VLCS datasets, respectively.
- Run the code with command:
nohup sh run.sh > run.txt 2>&1 &
- Check results in logs/(dataset)_(network)/(target domain)/(time)/logs.txt .
Citation
If you find our code or idea useful for your research, please consider citing our work.
@article{yuan2021collaborative,
title={Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization},
author={Yuan, Junkun and Ma, Xu and Chen, Defang and Kuang, Kun and Wu, Fei and Lin, Lanfen},
journal={arXiv e-prints},
pages={arXiv--2110},
year={2021}
}
Contact
If you have any questions, feel free to contact us through email ([email protected] or [email protected]) or GitHub issues. Thanks!
References
[1] Li, Da, et al. "Deeper, broader and artier domain generalization." Proceedings of the IEEE international conference on computer vision. 2017.
[2] Fang, Chen, Ye Xu, and Daniel N. Rockmore. "Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias." Proceedings of the IEEE International Conference on Computer Vision. 2013.
[3] Huang, Zeyi, et al. "Self-challenging improves cross-domain generalization." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. Springer International Publishing, 2020.