cGANs with Multi-Hinge Loss

Code

• github.com/ilyakava/BigGAN-PyTorch
  OfficialIn paperpytorch★ 0
• github.com/ilyakava/gan
  OfficialIn papertf★ 0
• github.com/MindSpore-paper-code-3/code6/tree/main/CGAN
  mindspore★ 0

Abstract

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code available at https://github.com/ilyakava/gan.

Tasks

Benchmark Results

Reproductions