{"type":"ScholarlyArticle","title":"Recent Advances in Concept Drift Adaptation Methods for Deep Learning","authors":[{"name":"Liheng Yuan","slug":"liheng-yuan","given":"Liheng","family":"Yuan","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Heng Li","slug":"heng-li","given":"Heng","family":"Li","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Beihao Xia","slug":"beihao-xia","given":"Beihao","family":"Xia","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Cuiying Gao","slug":"cuiying-gao","given":"Cuiying","family":"Gao","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Mingyue Liu","slug":"mingyue-liu","given":"Mingyue","family":"Liu","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Wei Yuan","slug":"wei-yuan","given":"Wei","family":"Yuan","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"},{"name":"Xinge You","slug":"xinge-you","given":"Xinge","family":"You","affiliation":"Huazhong University of Science and Technology {ylh , liheng , xbh hust ,"}],"abstract":"In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e.g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i.e., model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category.  Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.","doi":"10.24963/ijcai.2022/788","url":"https://ijcai.org/proceedings/2022/788","pdf":"https://ijcai.org/proceedings/2022/0788.pdf","markdown":"https://ijcai.org/proceedings/2022/788.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/788","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":788,"pages":{"first":5654,"last":5661},"keywords":["Survey Track: Data Mining","Survey Track: -","Survey Track: Machine Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987276","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985419","length_sec":636,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Andresini et al., 2021] Giuseppina Andresini, Annalisa Ap- pice, Corrado Loglisci, Vincenzo Belvedere, Domenico Redavid, and Donato Malerba. 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