https://github.com/minkaixu/sobolevwassersteingan
https://github.com/minkaixu/sobolevwassersteingan
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/minkaixu/sobolevwassersteingan
- Owner: MinkaiXu
- Created: 2020-12-06T15:28:21.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-02-21T03:25:23.000Z (over 4 years ago)
- Last Synced: 2025-01-22T07:28:20.623Z (5 months ago)
- Language: Python
- Size: 44.9 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Sobolev Wasserstein GAN
=====================================
This repo contains a reference implementation for SWGAN as described in the paper:
> Towards Generalized Implementation of Wasserstein Distance in GANs
> [Minkai Xu](https://minkaixu.com/), Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu
> AAAI Conference on Artificial Intelligence (AAAI), 2021.
> Paper: [https://arxiv.org/abs/2012.03420](https://arxiv.org/abs/2012.03420)
The implementation is built upon the repo [WGAN-GP](https://github.com/igul222/improved_wgan_training), code for reproducing experiments in ["Improved Training of Wasserstein GANs"](https://arxiv.org/abs/1704.00028).
## Prerequisites
- Python, NumPy, TensorFlow, SciPy, Matplotlib
- A recent NVIDIA GPU
## Models
Configuration for all models is specified in a list of constants at the top of
the file. Two models should work "out of the box":
- `python gan_toy.py`: Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).
For the other models, edit the file to specify the path to the dataset in
`DATA_DIR` before running. Each model's dataset is publicly available; the
download URL is in the file.
- `python gan_cifar_resnet.py`: CIFAR-10
## Citing
If you find SWGAN useful in your research, please consider citing the following two papers:
```
@article{xu2020towards,
title={Towards Generalized Implementation of Wasserstein Distance in GANs},
author={Xu, Minkai and Zhou, Zhiming and Lu, Guansong and Tang, Jian and Zhang, Weinan and Yu, Yong},
journal={AAAI Conference on Artificial Intelligence (AAAI), 2021.},
year={2020}
}
```
```
@article{gulrajani2017improved,
title={Improved training of wasserstein gans},
author={Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron},
journal={arXiv preprint arXiv:1704.00028},
year={2017}
}
```