{"type":"ScholarlyArticle","title":"Survey on Online Streaming Continual Learning","authors":[{"name":"Nuwan Gunasekara","slug":"nuwan-gunasekara","given":"Nuwan","family":"Gunasekara","affiliation":"AI Institute , University of Waikato"},{"name":"Bernhard Pfahringer","slug":"bernhard-pfahringer","given":"Bernhard","family":"Pfahringer","affiliation":"AI Institute , University of Waikato"},{"name":"Heitor Murilo Gomes","slug":"heitor-murilo-gomes","given":"Heitor Murilo","family":"Gomes","affiliation":"Victoria University of Wellington"},{"name":"Albert Bifet","slug":"albert-bifet","given":"Albert","family":"Bifet","affiliation":"AI Institute , University of Waikato; LTCI , Télécom Paris , IP Paris"}],"abstract":"Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning algorithm should adapt to input data distribution shifts without sacrificing accuracy. These distribution shifts are known as ”concept drifts” in the literature. SL provides many supervised, semi-supervised, and unsupervised methods for detecting and adjusting to concept drift. On the other hand, Continual Learning (CL) attempts to preserve previous knowledge while performing well on the current concept when confronted with concept drift. In Online Continual Learning (OCL), this learning happens online. This survey explores the intersection of those two online learning paradigms to find synergies. We identify this intersection as Online Streaming Continual Learning (OSCL). The study starts with a gentle introduction to SL and then explores CL. Next, it explores OSCL from SL and OCL perspectives to point out new research trends and give directions for future research.","doi":"10.24963/ijcai.2023/743","url":"https://ijcai.org/proceedings/2023/743","pdf":"https://ijcai.org/proceedings/2023/0743.pdf","markdown":"https://ijcai.org/proceedings/2023/743.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/743","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":743,"pages":{"first":6628,"last":6637},"keywords":["Survey: Machine Learning","Survey: Multidisciplinary Topics and Applications","Survey: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aljundi et al., 2019] Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, and Tinne Tuytelaars. 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