Wasserstein-2 Generative Networks

Abstract

We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost, i.e., the Wasserstein-2 distance. The algorithm uses input convex neural networks and a cycle-consistency regularization to approximate Wasserstein-2 distance. In contrast to popular entropic and quadratic regularizers, cycle-consistency does not introduce bias and scales well to high dimensions. From the theoretical side, we estimate the properties of the generative mapping fitted by our algorithm. From the practical side, we evaluate our algorithm on a wide range of tasks (image-to-image color transfer, latent space optimal transport, image-to-image style transfer, and domain adaptation).

Alexander Korotin

Assistant professor,
research scientist

My research interests include generative modeling, unpaired learning, optimal transport and Schrodinger bridges.