Generative Adversarial Networks (GAN)

IMPROVING GANS USING OPTIMAL TRANSPORT

gan OT

We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation.

 

Publications

  • Tim Salimans*, Han Zhang*, Alec Radford, Dimitris Metaxas: Improving GANs Using Optimal Transport
    ICLR, 2018.