RPSW

Official PyTorch implementation for paper: Sliced Wasserstein with Random-Path Projecting Directions

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

@article{nguyen2024sliced,
  title={Sliced Wasserstein with Random-Path Projecting Directions},
  author={Khai Nguyen and Nicola Bariletto and Nhat Ho},
  booktitle={nternational Conference on Machine Learning},
  year={2024},
  pdf={https://arxiv.org/pdf/2401.15889.pdf}
}

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.

Requirements

First, please install "power_spherical" package at https://github.com/nicola-decao/power_spherical, then run

pip install -r requirements.txt

What is included?

• Gradient flow
• Diffusion-GAN

Gradient flow

cd GradientFlow
python main.py

Diffusion-GAN

Please read the README file in the denoising-diffusion-gan folder.

Acknowledgment

Diffusion-GAN code is largely based on denoising-diffusion-gan.