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Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation

文献类型：期刊论文

作者Li, Liang1; Lu, Tongyu2; Sun, Yaoqi3; Gao, Yuhan4; Yan, Chenggang2; Hu, Zhenghui4; Huang, Qingming5,6
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2024-08-09
页码12
关键词Uncertainty Feature extraction Semantics Task analysis Training Adversarial machine learning Symbols Domain shifting progressive decision boundary self-learning unsupervised domain adaptation (UDA)
ISSN号2162-237X
DOI10.1109/TNNLS.2024.3431283
英文摘要Unsupervised domain adaptation (UDA) is attracting more attention from researchers for boosting the task-specific generalization on target domain. It focuses on addressing the domain shift between the labeled source domain and the unlabeled target domain. Recent biclassifier-based UDA models perform category-level alignment to reduce domain shift, and meanwhile, self-training is used for improving the discriminability of target instances. However, the error accumulation problem of instances with high semantic uncertainty may cause discriminability degradation and category-level misalignment. To solve this issue, we design the progressive decision boundary shifting algorithm, where stable category information of target instances is explored for learning a discriminability structure on target domain. Specifically, we first model the semantic uncertainty of instances by progressively shifting decision boundaries of category. Then, we introduce the uncertainty decoupling in a contrastive manner, where the discriminative information is learned from the source domain for instance with low semantic uncertainty. Furthermore, we minimize the predictive entropy of instances with high semantic uncertainty to reduce their prediction confidence. Extensive experiments on three popular datasets show that our model outperforms the current state-of-the-art (SOTA) UDA methods.
资助项目National Natural Science Foundation of China[62322211] ; National Natural Science Foundation of China[62336008] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[U21B2024] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2020108] ; The Pioneer and Leading Goose Research and Development Program of Zhejiang Province[2024C01023]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001288144900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39671]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yan, Chenggang; Hu, Zhenghui
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China
2.Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China
3.Hangzhou Dianzi Univ, Lishui Inst, Hangzhou, Zhejiang, Peoples R China
4.Beihang Univ, Hangzhou Innovat Inst, Beijing, Zhejiang, Peoples R China
5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Liang,Lu, Tongyu,Sun, Yaoqi,et al. Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2024:12.
APA Li, Liang.,Lu, Tongyu.,Sun, Yaoqi.,Gao, Yuhan.,Yan, Chenggang.,...&Huang, Qingming.(2024).Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA Li, Liang,et al."Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024):12.

入库方式： OAI收割

来源：计算技术研究所

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