{"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/autoencoding-under-normalization-constraints","title":"Autoencoding Under Normalization Constraints","arxiv_id":"2105.05735","date":"2021-05-12","proceeding":null,"authors":["Sangwoong Yoon","Yung-Kyun Noh","Frank Chongwoo Park"],"abstract":"Likelihood is a standard estimate for outlier detection. The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood. 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