{"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/adversarial-continual-learning","title":"Adversarial Continual Learning","arxiv_id":"2003.09553","date":"2020-03-21","proceeding":"ECCV 2020 8","authors":["Sayna Ebrahimi","Franziska Meier","Roberto Calandra","Trevor Darrell","Marcus Rohrbach"],"abstract":"Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. 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