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

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023

[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

1. Prepare the datasets.
2. Update root_dir in configs/datasets/dg/pacs.yaml/ and configs/datasets/dg/vlcs.yaml/ with the paths of PACS and VLCS datasets, respectively.
3. Run the code with command:

nohup sh run.sh > run.txt 2>&1 &

4. Check results in: logs/dataset_network/target domain/date/logs.txt.

Updates

• [01/16/2022] We uploaded a new arXiv version. See https://arxiv.org/abs/2110.06736.

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 (yuanjk@zju.edu.cn or maxu@zju.edu.cn) 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.