{"type":"ScholarlyArticle","title":"Core-Structures-Guided Multi-Modal Classification Neural Architecture Search","authors":[{"name":"Pinhan Fu","slug":"pinhan-fu","given":"Pinhan","family":"Fu","affiliation":"Institute of Big Data Science and Industry , Shanxi University , Taiyuan 030006 , China"},{"name":"Xinyan Liang","slug":"xinyan-liang","given":"Xinyan","family":"Liang","affiliation":"Institute of Big Data Science and Industry , Shanxi University , Taiyuan 030006 , China"},{"name":"Tingjin Luo","slug":"tingjin-luo","given":"Tingjin","family":"Luo","affiliation":"College of Science , National University of Defense Technology , Changsha , China 410073 , China"},{"name":"Qian Guo","slug":"qian-guo","given":"Qian","family":"Guo","affiliation":"School of Computer Science and Technology , Taiyuan University of Science and Technology , Taiyuan 030024 , China"},{"name":"Yayu Zhang","slug":"yayu-zhang","given":"Yayu","family":"Zhang","affiliation":"Institute of Big Data Science and Industry , Shanxi University , Taiyuan 030006 , China"},{"name":"Yuhua Qian","slug":"yuhua-qian","given":"Yuhua","family":"Qian","affiliation":"Institute of Big Data Science and Industry , Shanxi University , Taiyuan 030006 , China"}],"abstract":"The multi-modal classification methods based on neural architecture search (NAS-MMC) can automatically learn a satisfied classifier from a given multi-modal search space. However, as the number of multi-modal features and fusion operators increases, the complexity of search space has increased dramatically. Rapidly identifying the satisfied fusion model from this vast space is very challenging. In this paper, we propose an efficient NAS-MMC method based on an idea of shrink-and-expansion search space, called core-structure-guided neural architecture search (CSG-NAS). Specifically, an evolutionary algorithm is first used to find core structures from a shrunk space (also called core structure search space) determined by high-quality features and fusion operators. Then a local search algorithm is used to find the optimal MMC model from the expanded space determined by the discovered core structures and the rest features as well as fusion operators. Moreover, a knowledge transfer strategy is introduced to further improve the overall performance and efficiency of the entire search process. Finally, extensive experimental results demonstrate the effectiveness of our CSG-NAS, attaining the superiority of classification performance, training efficiency and model complexity, compared to state-of-the-art  ompetitors on several public benchmark multi-modal tasks. The source code is available at https://github.com/fupinhan123/CSG-NAS.","doi":"10.24963/ijcai.2024/440","url":"https://ijcai.org/proceedings/2024/440","pdf":"https://ijcai.org/proceedings/2024/0440.pdf","markdown":"https://ijcai.org/proceedings/2024/440.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/440","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":440,"pages":{"first":3980,"last":3988},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Evolutionary learning","Machine Learning: ML: Classification","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Arevalo et al., 2017] John Arevalo, Thamar Solorio, Manuel Montes-y Gómez, and FabioA. 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