{"type":"ScholarlyArticle","title":"A Density-driven Iterative Prototype Optimization for Transductive Few-shot Learning","authors":[{"name":"Jingcong Li","slug":"jingcong-li","given":"Jingcong","family":"Li","affiliation":"School of Software , South China Normal University"},{"name":"Chunjin Ye","slug":"chunjin-ye","given":"Chunjin","family":"Ye","affiliation":"School of Software , South China Normal University"},{"name":"Fei Wang","slug":"fei-wang","given":"Fei","family":"Wang","affiliation":"School of Software , South China Normal University"},{"name":"Jiahui Pan","slug":"jiahui-pan","given":"Jiahui","family":"Pan","affiliation":"School of Software , South China Normal University"}],"abstract":"Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model generalization ability with limited labeled data. Previous works usually attempt to construct class-specific prototypes and then predict novel classes using these prototypes. However, the feature distribution represented by the limited labeled data is coarse-grained, leading to large information gap between the labeled and unlabeled data as well as biases in the prototypes. In this paper, we investigate the correlation between sample quality and density, and propose a Density-driven Iterative Prototype Optimization to acquire high-quality prototypes, and further improve few-shot learning performance. Specifically, the proposed method consists of two optimization strategies. The similarity-evaluating strategy is for capturing the information gap between the labeled and unlabeled data by reshaping the feature manifold for the novel feature distribution.  The density-driven strategy is proposed to iteratively refine the prototypes in the direction of density growth. The proposed method could reach or even exceed the state-of-the-art performance on four benchmark datasets, including mini-ImageNet, tiered-ImageNet, CUB, and CIFAR-FS. The code will be available soon at https://github.com/tailofcat/DIPO.","doi":"10.24963/ijcai.2024/488","url":"https://ijcai.org/proceedings/2024/488","pdf":"https://ijcai.org/proceedings/2024/0488.pdf","markdown":"https://ijcai.org/proceedings/2024/488.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/488","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":488,"pages":{"first":4416,"last":4424},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2018] Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi. 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