Improving GANs Using Optimal Transport
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.
Cite
Text
Salimans et al. "Improving GANs Using Optimal Transport." International Conference on Learning Representations, 2018.Markdown
[Salimans et al. "Improving GANs Using Optimal Transport." International Conference on Learning Representations, 2018.](https://mlanthology.org/iclr/2018/salimans2018iclr-improving/)BibTeX
@inproceedings{salimans2018iclr-improving,
title = {{Improving GANs Using Optimal Transport}},
author = {Salimans, Tim and Zhang, Han and Radford, Alec and Metaxas, Dimitris},
booktitle = {International Conference on Learning Representations},
year = {2018},
url = {https://mlanthology.org/iclr/2018/salimans2018iclr-improving/}
}