{"type":"ScholarlyArticle","title":"Class-Specific Semantic Generation and Reconstruction Learning for Open Set Recognition","authors":[{"name":"Liu Haoyang","slug":"liu-haoyang","given":"Liu","family":"Haoyang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data ( Hefei University of Technology) , Ministry of Education; School of Computer Science and Information Engineering , Hefei University of Technology"},{"name":"Yaojin Lin","slug":"yaojin-lin","given":"Yaojin","family":"Lin","affiliation":"School of Computer Science , Minnan Normal University; Key Laboratory of Data Science and Intelligence Application , Minnan Normal University"},{"name":"Peipei Li","slug":"peipei-li","given":"Peipei","family":"Li","affiliation":"Key Laboratory of Knowledge Engineering with Big Data ( Hefei University of Technology) , Ministry of Education; School of Computer Science and Information Engineering , Hefei University of Technology"},{"name":"Jun Hu","slug":"jun-hu","given":"Jun","family":"Hu","affiliation":"School of Computing , National University of Singapore"},{"name":"Xuegang Hu","slug":"xuegang-hu","given":"Xuegang","family":"Hu","affiliation":"Key Laboratory of Knowledge Engineering with Big Data ( Hefei University of Technology) , Ministry of Education; School of Computer Science and Information Engineering , Hefei University of Technology; Anhui Province Key Laboratory of Industry Safety and Emergency Technology"}],"abstract":"Open set recognition is a crucial research theme for open-environment machine learning. For this problem, a common solution is to learn compact representations of known classes and identify unknown samples by measuring deviations from these known classes. However, the aforementioned methods (1) lack open training consideration, which is detrimental to the fitting of known classes, and (2) recognize unknown classes on an inadequate basis, which limits the accuracy of recognition. In this study, we propose an open reconstruction learning framework that learns a union boundary region of known classes to characterize unknown space. This facilitates the isolation of known space from unknown space to represent known classes compactly and provides a more reliable recognition basis from the perspective of both known and unknown space. Specifically, an adversarial constraint is used to generate class-specific boundary samples. Then, the known classes and approximate unknown space are fitted with manifolds represented by class-specific auto-encoders. Finally, the auto-encoders output the reconstruction error in terms of known and unknown spaces to recognize samples. Extensive experimental results show that the proposed method outperforms existing advanced methods and achieves new stateof-the-art performance. The code is available at https://github.com/Ashowman98/CSGRL.","doi":"10.24963/ijcai.2024/226","url":"https://ijcai.org/proceedings/2024/226","pdf":"https://ijcai.org/proceedings/2024/0226.pdf","markdown":"https://ijcai.org/proceedings/2024/226.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/226","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":"Data Mining","track":"Main Track","number":226,"pages":{"first":2045,"last":2053},"keywords":["Data Mining: DM: Anomaly/outlier detection","Data Mining: DM: Other"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2023] Desheng Cai, Shengsheng Qian, Quan Fang, Jun Hu, and Changsheng Xu. 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