  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.
