{"type":"ScholarlyArticle","title":"Alleviating Imbalanced Pseudo-label Distribution: Self-Supervised Multi-Source Domain Adaptation with Label-specific Confidence","authors":[{"name":"Shuai Lü","slug":"shuai-lu","given":"Shuai","family":"Lu","affiliation":""},{"name":"Meng Kang","slug":"meng-kang","given":"Meng","family":"Kang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"},{"name":"Ximing Li","slug":"ximing-li","given":"Ximing","family":"Li","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , China; College of Computer Science and Technology , Jilin University , China; College of Software , Jilin University , China"}],"abstract":"The existing self-supervised Multi-Source Domain Adaptation (MSDA) methods often suffer an imbalanced characteristic among the distribution of pseudo-labels. Such imbalanced characteristic results in many labels with too many or too few pseudo-labeled samples on the target domain, referred to as easy-to-learn label and hard-to-learn label, respectively. Both of these labels hurt the generalization performance on the target domain. To alleviate this problem, in this paper we propose a novel multi-source domain adaptation method, namely Self-Supervised multi-Source Domain Adaptation with Label-specific Confidence (S3DA-LC). Specifically, we estimate the label-specific confidences, i.e., the learning difficulties of labels, and adopt them to generate the pseudo-labels for target samples, enabling to simultaneously constrain and enrich the pseudo supervised signals for easy-to-learn and hard-to-learn labels. We evaluate S3DA-LC on several benchmark datasets, indicating its superior performance compared with the existing MSDA baselines.","doi":"10.24963/ijcai.2024/516","url":"https://ijcai.org/proceedings/2024/516","pdf":"https://ijcai.org/proceedings/2024/0516.pdf","markdown":"https://ijcai.org/proceedings/2024/516.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/516","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":516,"pages":{"first":4669,"last":4677},"keywords":["Machine Learning: ML: Classification","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Multi-task and transfer learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2021] Sk Miraj Ahmed, Dripta S. 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