{"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/barlow-twins-self-supervised-learning-via","title":"Barlow Twins: Self-Supervised Learning via Redundancy Reduction","arxiv_id":"2103.03230","date":"2021-03-04","proceeding":null,"authors":["Jure Zbontar","Li Jing","Ishan Misra","Yann Lecun","Stéphane Deny"],"abstract":"Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. 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