https://github.com/nocotan/pytorch-lightning-gans
Collection of PyTorch Lightning implementations of Generative Adversarial Network varieties presented in research papers.
https://github.com/nocotan/pytorch-lightning-gans
deep-learning gans generative-adversarial-network neural-networks pytorch pytorch-implementation pytorch-lightning
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Collection of PyTorch Lightning implementations of Generative Adversarial Network varieties presented in research papers.
- Host: GitHub
- URL: https://github.com/nocotan/pytorch-lightning-gans
- Owner: nocotan
- License: mit
- Created: 2019-08-15T10:44:44.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-04T02:03:43.000Z (over 3 years ago)
- Last Synced: 2024-07-30T18:40:43.511Z (about 1 year ago)
- Topics: deep-learning, gans, generative-adversarial-network, neural-networks, pytorch, pytorch-implementation, pytorch-lightning
- Language: Python
- Homepage:
- Size: 42 KB
- Stars: 163
- Watchers: 5
- Forks: 16
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyTorch Lightning GANs
[](https://zenodo.org/badge/latestdoi/202523756)
[](https://github.com/nocotan/pytorch-lightning-gans/blob/master/LICENSE)
Collection of PyTorch Lightning implementations of Generative Adversarial Network varieties presented in research papers.
## Installation
```bash
$ pip install -r requirements.txt
```
## Example
The minimum code for training GAN is as follows:
```python
from pytorch_lightning.trainer import Trainer
from models import GAN
model = GAN()
trainer = Trainer()
trainer.fit(model)
```
or you can run the following command:
```bash
$ python models/gan.py --gpus=2
```
## Implementations
* ACGAN: Auxiliary Classifier GAN (Odena et al.)
* BEGAN: Boundary equilibrium generative adversarial networks (Berthelot et al.)
* DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Radford et al.)
* GAN: Generative Adversarial Networks (Goodfellow et al.)
* LSGAN: Least squares generative adversarial networks (Mao et al.)
* WGAN: Wasserstein GAN (Arjovsky et al.)
* WGAN-GP: Improved Training of Wasserstein GANs (Gulrajani et al.)
## Acknowledgements
This repository is highly inspired by [PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN) repository.
## References
* Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
* Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
* Odena, Augustus, Christopher Olah, and Jonathon Shlens. "Conditional image synthesis with auxiliary classifier gans." International conference on machine learning. PMLR, 2017.
* Berthelot, David, Thomas Schumm, and Luke Metz. "Began: Boundary equilibrium generative adversarial networks." arXiv preprint arXiv:1703.10717 (2017).
* Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.
* Arjovsky, Martin, Soumith Chintala, and Léon Bottou. "Wasserstein generative adversarial networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. 2017.
* Gulrajani, Ishaan, et al. "Improved training of wasserstein gans." Advances in neural information processing systems. 2017.
## Citation
```bibtex
@software{https://doi.org/10.5281/zenodo.4404867,
doi = {10.5281/ZENODO.4404867},
url = {https://zenodo.org/record/4404867},
author = {Masanari Kimura},
title = {pytorch-lightning-gans},
publisher = {Zenodo},
year = {2020},
copyright = {Open Access}
}
```