{"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/disentangling-identifiable-features-from","title":"Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA","arxiv_id":"2106.09620","date":"2021-06-17","proceeding":"NeurIPS 2021 12","authors":["Hermanni Hälvä","Sylvain Le Corff","Luc Lehéricy","Jonathan So","Yongjie Zhu","Elisabeth Gassiat","Aapo Hyvarinen"],"abstract":"We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). 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