{"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/robust-one-class-classification-with-signed","title":"Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks","arxiv_id":"2303.01978","date":"2023-01-26","proceeding":null,"authors":["Louis Bethune","Paul Novello","Thibaut Boissin","Guillaume Coiffier","Mathieu Serrurier","Quentin Vincenot","Andres Troya-Galvis"],"abstract":"We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. 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