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Regularizing Generative Adversarial Networks under Limited Data

[Project Page][Paper]

Implementation for our GAN regularization method. The proposed regularization 1) improves the performance of GANs under limited training data, and 2) complements the exisiting data augmentation approches.

Please note that this is not an officially supported Google product.

Paper

Please cite our paper if you find the code or dataset useful for your research.

Regularizing Generative Adversarial Networks under Limited Data<br> Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang<br> Computer Vision and Pattern Recognition (CVPR), 2021

@inproceedings{lecamgan,
  author = {Tseng, Hung-Yu and Jiang, Lu and Liu, Ce and Yang, Ming-Hsuan and Yang, Weilong},
  title = {Regularing Generative Adversarial Networks under Limited Data},
  booktitle = {CVPR},
  year = {2021}
}
<div align="center"> <img src="imgs/framework.png" width="800px" /> </div>

Installation and Usage

We provide three implementations: biggan_cifar, biggan_imagenet, and stylegan2. Plesase refer to the README.md file under each sub-folder for the installation and usage guides.