{"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/generalisation-guarantees-for-continual","title":"Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent","arxiv_id":"2006.11942","date":"2020-06-21","proceeding":null,"authors":["Mehdi Abbana Bennani","Thang Doan","Masashi Sugiyama"],"abstract":"In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent was proposed to tackle the challenge. However, no theoretical guarantees have been proven yet. 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