{"type":"ScholarlyArticle","title":"Deep Partial Multi-Label Learning with Graph Disambiguation","authors":[{"name":"Haobo Wang","slug":"haobo-wang","given":"Haobo","family":"Wang","affiliation":"Zhejiang University"},{"name":"Shisong Yang","slug":"shisong-yang","given":"Shisong","family":"Yang","affiliation":"Beijing University of Technology"},{"name":"Gengyu Lyu","slug":"gengyu-lyu","given":"Gengyu","family":"Lyu","affiliation":"Beijing University of Technology"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"Wuhan University"},{"name":"Tianlei Hu","slug":"tianlei-hu","given":"Tianlei","family":"Hu","affiliation":"Zhejiang University"},{"name":"Ke Chen","slug":"ke-chen","given":"Ke","family":"Chen","affiliation":"Zhejiang University"},{"name":"Songhe Feng","slug":"songhe-feng","given":"Songhe","family":"Feng","affiliation":"Beijing Jiaotong University"},{"name":"Gang Chen","slug":"gang-chen","given":"Gang","family":"Chen","affiliation":"Zhejiang University"}],"abstract":"In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods.","doi":"10.24963/ijcai.2023/479","url":"https://ijcai.org/proceedings/2023/479","pdf":"https://ijcai.org/proceedings/2023/0479.pdf","markdown":"https://ijcai.org/proceedings/2023/479.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/479","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":479,"pages":{"first":4308,"last":4316},"keywords":["Machine Learning: ML: Multi-label"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Junwen Bai, Shufeng Kong, and Carla P. 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