Discriminator optimal transport

Code

• github.com/AkinoriTanaka-phys/DOT
  OfficialIn papertf★ 0

Abstract

Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution p and the generator distribution p_G. It implies that the trained discriminator can approximate optimal transport (OT) from p_G to p.Based on some experiments and a bit of OT theory, we propose a discriminator optimal transport (DOT) scheme to improve generated images. We show that it improves inception score and FID calculated by un-conditional GAN trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN by ImageNet.

Reproductions