{"type":"ScholarlyArticle","title":"ParsNets: A Parsimonious Composition of Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning","authors":[{"name":"Jingcai Guo","slug":"jingcai-guo","given":"Jingcai","family":"Guo","affiliation":"Department of Computing , The Hong Kong Polytechnic University; Hong Kong Polytechnic University Shenzhen Research Institute"},{"name":"Qihua Zhou","slug":"qihua-zhou","given":"Qihua","family":"Zhou","affiliation":"Department of Computing , The Hong Kong Polytechnic University; College of Computer Science and Software Engineering , Shenzhen University"},{"name":"Xiaocheng Lu","slug":"xiaocheng-lu","given":"Xiaocheng","family":"Lu","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Ruibin Li","slug":"ruibin-li","given":"Ruibin","family":"Li","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Ziming Liu","slug":"ziming-liu","given":"Ziming","family":"Liu","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Bo Han","slug":"bo-han","given":"Bo","family":"Han","affiliation":"Department of Computer Science , Hong Kong Baptist University"},{"name":"Junyang Chen","slug":"junyang-chen","given":"Junyang","family":"Chen","affiliation":"Department of Computing , The Hong Kong Polytechnic University; College of Computer Science and Software Engineering , Shenzhen University"},{"name":"Xin Xie","slug":"xin-xie","given":"Xin","family":"Xie","affiliation":"College of Intelligence and Computing , Tianjin University"},{"name":"Song Guo","slug":"song-guo","given":"Song","family":"Guo","affiliation":"Department of Computer Science and Engineering , HKUST"}],"abstract":"This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, in which we are interested in learning a composition of on-device friendly linear networks, each with orthogonality and low-rankness properties, to achieve equivalent or better performance against deep models. Concretely, we first refactor the core module of ZSL, i.e., the visual-semantics mapping function, into several base linear networks that correspond to diverse components of the semantic space, wherein the complex nonlinearity can be collapsed into simple local linearities. Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes. To enhance the model's adaptability and counterbalance the over-/under-fittings, a set of sample-wise indicators is employed to select a sparse subset from these base linear networks to form a composite semantic predictor for each sample. Notably, maximal margin geometry can guarantee the diversity of features and, meanwhile, local linearities guarantee efficiency. Thus, our ParsNets can generalize better to unseen classes and can be deployed flexibly on resource-constrained devices.","doi":"10.24963/ijcai.2024/449","url":"https://ijcai.org/proceedings/2024/449","pdf":"https://ijcai.org/proceedings/2024/0449.pdf","markdown":"https://ijcai.org/proceedings/2024/449.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/449","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":449,"pages":{"first":4062,"last":4070},"keywords":["Machine Learning: ML: Cost-sensitive learning","Machine Learning: ML: Ensemble methods","Machine Learning: ML: Learning sparse models","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2015] Zeynep Akata, Scott Reed, Daniel Wal- ter, Honglak Lee, and Bernt Schiele. 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