{"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/overcoming-catastrophic-forgetting-in-neural","title":"Overcoming catastrophic forgetting in neural networks","arxiv_id":"1612.00796","date":"2016-12-02","proceeding":null,"authors":["James Kirkpatrick","Razvan Pascanu","Neil Rabinowitz","Joel Veness","Guillaume Desjardins","Andrei A. Rusu","Kieran Milan","John Quan","Tiago Ramalho","Agnieszka Grabska-Barwinska","Demis Hassabis","Claudia Clopath","Dharshan Kumaran","Raia Hadsell"],"abstract":"The ability to learn tasks in a sequential fashion is crucial to the\ndevelopment of artificial intelligence. Neural networks are not, in general,\ncapable of this and it has been widely thought that catastrophic forgetting is\nan inevitable feature of connectionist models. We show that it is possible to\novercome this limitation and train networks that can maintain expertise on\ntasks which they have not experienced for a long time. Our approach remembers\nold tasks by selectively slowing down learning on the weights important for\nthose tasks. 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