{"type":"ScholarlyArticle","title":"Joint Input and Output Coordination for Class-Incremental Learning","authors":[{"name":"Shuai Wang","slug":"shuai-wang","given":"Shuai","family":"Wang","affiliation":"Institute of Artificial Intelligence , School of Computer Science , Wuhan University , China; Hubei Luojia Laboratory , Wuhan , China"},{"name":"Yibing Zhan","slug":"yibing-zhan","given":"Yibing","family":"Zhan","affiliation":"JD Explore Academy , JD.com , Inc. , China"},{"name":"Yong Luo","slug":"yong-luo","given":"Yong","family":"Luo","affiliation":"Institute of Artificial Intelligence , School of Computer Science , Wuhan University , China; Hubei Luojia Laboratory , Wuhan , China"},{"name":"Han Hu","slug":"han-hu","given":"Han","family":"Hu","affiliation":"School of Information and Electronics , Beijing Institute of Technology , China"},{"name":"Wei Yu","slug":"wei-yu","given":"Wei","family":"Yu","affiliation":"Institute of Artificial Intelligence , School of Computer Science , Wuhan University , China"},{"name":"Yonggang Wen","slug":"yonggang-wen","given":"Yonggang","family":"Wen","affiliation":"College of Computing & Data Science , Nanyang Technological University , Singapore"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"College of Computing & Data Science , Nanyang Technological University , Singapore"}],"abstract":"Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the class bias problem, and 2) alleviate the mutual interference between new and old tasks, and 3) consider the problem of class bias within tasks. In light of the above issues, we analyze the cause of class bias in incremental learning, as well as the drawbacks of existing approaches, and propose a joint input and output coordination (JIOC) mechanism to address these issues. This mechanism assigns different weights to different categories of data according to the gradient of the output score, and uses knowledge distillation (KD) to reduce the mutual interference between the outputs of old and new tasks. The proposed mechanism is general and flexible, and can be incorporated into different incremental learning approaches that use memory storage. Extensive experiments show that our mechanism can significantly improve their performance.","doi":"10.24963/ijcai.2024/565","url":"https://ijcai.org/proceedings/2024/565","pdf":"https://ijcai.org/proceedings/2024/0565.pdf","markdown":"https://ijcai.org/proceedings/2024/565.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/565","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":565,"pages":{"first":5108,"last":5116},"keywords":["Machine Learning: ML: Incremental learning"],"videos":[],"references":[{"position":1,"raw":"[Ahn et al., 2021] Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon. 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