Wasserstein Iterative Networks for Barycenter Estimation

Understand

Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way.

• In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model.
• Previous approaches rely on regularization (entropic/quadratic) which introduces bias or on input convex neural networks which are not expressive enough for large-scale tasks.
• In contrast, our algorithm does not introduce bias and allows using arbitrary neural networks.