This repository showcases the mode collapse problem of GANs for different loss functions on a 2D toy example distribution.
| Standard GAN loss | Non-saturating GAN loss | Non-saturating GAN loss + top-k | Hinge GAN loss | Least squares GAN loss |
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| Wasserstein GAN loss | Wasserstein GAN loss + weight clipping | Wasserstein GAN loss + spec. norm | Wasserstein GAN loss + GP | Wasserstein GAN loss + GP + top-k |
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To install all necessary dependencies simply run:
pip install -r requirements.txtTo reproduces the achieved results run the main.py script with the corresponding arguments. Train the discriminater till optimality when using Wasserstein loss by updating it 5 times per generator update leading to more reliable gradients of Wasserstein. Plain Wasserstein requires weight clipping to [-0.01,+0.01] to enforce Lipschitz constraint.
| Argument | Default value | Info |
|---|---|---|
--device |
'cuda' | Set device to be utilized (cuda or cpu) |
--epochs |
500 | Training epochs to be performed |
--d_updates |
1 | Discriminator updates per generator update |
--plot_frequency |
10 | Frequency of epochs to produce plots |
--lr |
0.0001 | Learning rate to be applied |
--latent_size |
32 | Size of latent vector to be utilized |
--samples |
10000 | Number of samples from the real distribution |
--batch_size |
500 | Batch size to be utilized |
--loss |
'standard' | GAN loss function to be used (standard, non-saturating, hinge, wasserstein, wasserstein-gp or least-squares) |
--spectral_norm |
False | If set use spectral norm to stabilize discriminator |
--clip_weights |
0 | If > 0., weights will be clipped to [-clip_weights, +clip_weights] |
--topk |
False | If set top-k training is utilized after 0.5 of the epochs to be performed |
@inproceedings{Goodfellow2014,
title = {Generative Adversarial Nets},
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
booktitle = {Advances in Neural Information Processing Systems},
volume = {27},
year = {2014}
}@inproceedings{Arjovsky2017,
title={Wasserstein generative adversarial networks},
author={Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
booktitle={International conference on machine learning},
pages={214--223},
year={2017},
organization={PMLR}
}@inproceedings{Gulrajani2017,
title={Improved training of wasserstein GANs},
author={Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron},
booktitle={Proceedings of the 31st International Conference on Neural Information Processing Systems},
pages={5769--5779},
year={2017}
}@article{Lim2017,
title={Geometric gan},
author={Lim, Jae Hyun and Ye, Jong Chul},
journal={arXiv preprint arXiv:1705.02894},
year={2017}
}@inproceedings{Mao2017,
title={Least squares generative adversarial networks},
author={Mao, Xudong and Li, Qing and Xie, Haoran and Lau, Raymond YK and Wang, Zhen and Paul Smolley, Stephen},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2794--2802},
year={2017}
}@inproceedings{Sinha2020,
title = {Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples},
author = {Sinha, Samarth and Zhao, Zhengli and ALIAS PARTH GOYAL, Anirudh Goyal and Raffel, Colin A and Odena, Augustus},
booktitle = {Advances in Neural Information Processing Systems},
volume = {33},
year = {2020}
}
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