{"type":"ScholarlyArticle","title":"Dynamic against Dynamic: An Open-Set Self-Learning Framework","authors":[{"name":"Haifeng Yang","slug":"haifeng-yang","given":"Haifeng","family":"Yang","affiliation":"Nanjing University of Aeronautics and Astronautics; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Chuanxing Geng","slug":"chuanxing-geng","given":"Chuanxing","family":"Geng","affiliation":"Nanjing University of Aeronautics and Astronautics; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence; Hong Kong Baptist University"},{"name":"Pong C. Yuen","slug":"pong-c-yuen","given":"Pong C.","family":"Yuen","affiliation":"Hong Kong Baptist University"},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":"Nanjing University of Aeronautics and Astronautics; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"}],"abstract":"In open set recognition, existing methods generally learn statically fixed decision boundaries to reject unknown classes. Though they have achieved promising results, such decision boundaries are evidently insufficient for universal unknown classes in dynamic and open scenarios as they can potentially appear at any position in the feature space. Moreover, these methods just simply reject unknown class samples during testing without any effective utilization for them. In fact, such samples completely can constitute the true instantiated representation of the unknown classes to further enhance the model's performance. To address these issues, this paper proposes a novel dynamic against dynamic idea, i.e., dynamic method against dynamic changing open-set world, where an open-set self-learning (OSSL) framework is correspondingly developed. OSSL starts with a good closed-set classifier trained by known classes and utilizes available test samples for model adaptation during testing, thus gaining the adaptability to changing data distributions. In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes. Our method establishes new performance milestones respectively in almost all standard and cross-data benchmarks.","doi":"10.24963/ijcai.2024/587","url":"https://ijcai.org/proceedings/2024/587","pdf":"https://ijcai.org/proceedings/2024/0587.pdf","markdown":"https://ijcai.org/proceedings/2024/587.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/587","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":587,"pages":{"first":5307,"last":5315},"keywords":["Computer Vision: CV: Machine learning for vision","Machine Learning: ML: Classification","Machine Learning: ML: Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Bartler et al., 2022] Alexander Bartler, Andre Bühler, Felix Wiewel, Mario Döbler, and Bin Yang. 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