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Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford, Dimitris Metaxas
Feb 15, 2018 (modified: Feb 24, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract: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.
TL;DR:An extension of GANs combining optimal transport in primal form with an energy distance defined in an adversarially learned feature space.
Keywords:GAN, generative modeling, adversarial, optimal transport
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