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LSGAN (ICCV'2017)

Least Squares Generative Adversarial Networks

Task: Unconditional GANs

Abstract

Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson χ2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on five scene datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.

Results and models

LSGAN 64x64, CelebA-Cropped
Model Dataset SWD MS-SSIM FID Download
LSGAN 64x64 CelebA-Cropped 6.16, 6.83, 37.64/16.87 0.3216 11.9258 model| log
LSGAN 64x64 LSUN-Bedroom 5.66, 9.0, 18.6/11.09 0.0671 30.7390 model| log
LSGAN 128x128 CelebA-Cropped 21.66, 9.83, 16.06, 70.76/29.58 0.3691 38.3752 model| log
LSGAN 128x128 LSUN-Bedroom 19.52, 9.99, 7.48, 14.3/12.82 0.0612 51.5500 model| log

Citation

@inproceedings{mao2017least,
  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},
  url={https://openaccess.thecvf.com/content_iccv_2017/html/Mao_Least_Squares_Generative_ICCV_2017_paper.html},
}