Improving GANs Using Optimal Transport
This work addresses training instability in GANs for image generation, offering a novel approach that improves results on standard benchmarks.
The authors tackled the problem of training generative adversarial networks (GANs) by introducing OT-GAN, which minimizes a new metric combining optimal transport and energy distance, resulting in highly stable training with large mini-batches and state-of-the-art performance on image generation benchmarks.
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.