{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wasserstein-iterative-networks-for-barycenter","title":"Wasserstein Iterative Networks for Barycenter Estimation","arxiv_id":"2201.12245","date":"2022-01-28","proceeding":null,"authors":["Alexander Korotin","Vage Egiazarian","Lingxiao Li","Evgeny Burnaev"],"abstract":"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. 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