MFSWB

Official PyTorch implementation for paper: Towards Marginal Fairness Sliced Wasserstein Barycenter

Details of the model architecture and experimental results can be found in our papers.

@inproceedings{nguyen2025towards,
	title={Towards Marginal Fairness Sliced Wasserstein Barycenter},
	author={Nguyen, Khai and Nguyen, Hai and Ho, Nhat},
	booktitle={The Thirteenth International Conference on Learning Representations},
	year={2025},
	url={https://openreview.net/forum?id=NQqJPPCesd}
}

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

This implementation is made by Khai Nguyen (Gaussian Averaging, Color Harmonization, Point Cloud Averaging) and Hai Nguyen (Sliced Wasserstein Autoencoder).

Requirements

To install the required python packages, run

pip install -r requirements.txt

What is included?

• Gaussian Averaging
• Point Cloud Averaging
• Color Harmonization
• Sliced Wasserstein Autoencoder

Gaussian Averaging

cd GaussianAveraging;
python gaussian_example.py;

Point Cloud Averaging

cd PointCloudAveraging;
mkdir saved;
python main_point.py

Color Harmonization

cd ColorHarmonization;
python main.py;

Sliced Wasserstein Autoencoder

See README.md in SWAE folder