{"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/meta-aggregating-networks-for-class-1","title":"Adaptive Aggregation Networks for Class-Incremental Learning","arxiv_id":"2010.05063","date":"2020-10-10","proceeding":"CVPR 2021 1","authors":["Yaoyao Liu","Bernt Schiele","Qianru Sun"],"abstract":"Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. 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