GGAN

Geometric GAN

Abstract

Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN.

Results and models

GGAN 64x64, CelebA-Cropped

Note: In the original implementation of GGAN, they set G_iters to 10. However our framework does not support G_iters currently, so we dropped the settings in the original implementation and conducted several experiments with our own settings. We have shown above the experiment results with the lowest fid score.
Original settings and our settings:

Citation

@article{lim2017geometric,
  title={Geometric gan},
  author={Lim, Jae Hyun and Ye, Jong Chul},
  journal={arXiv preprint arXiv:1705.02894},
  year={2017},
  url={https://arxiv.org/abs/1705.02894},
}