google/lecam-gan
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)
| repo name | google/lecam-gan |
| repo link | https://github.com/google/lecam-gan |
| homepage | |
| language | Jupyter Notebook |
| size (curr.) | 4511 kB |
| stars (curr.) | 48 |
| created | 2021-03-19 |
| license | Apache License 2.0 |
Regularizing Generative Adversarial Networks under Limited Data
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 Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang 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}
}
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.
