Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation

doi:10.1109/TNNLS.2022.3193289

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation
	Tian, Qing 1,2,3; Ma, Chuang 1,2; Cao, Meng 1,2; Wan, Jun4 ; Lei, Zhen4 ; Chen, Songcan 5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2022-07-27
页码	14
通讯作者	Chen, Songcan(s.chen@nuaa.edu.cn)
摘要	Unsupervised domain adaptation (UDA) is an emerging learning paradigm that models on unlabeled datasets by leveraging model knowledge built on other labeled datasets, in which the statistical distributions of these datasets are usually not identical. Formally, UDA is to leverage knowledge from a labeled source domain to promote an unlabeled target domain. Although there have been a variety of methods proposed to address the UDA problem, most of them are dedicated to single-source-to-single-target domain, while the works on single-source-to-multitarget domain are relatively rare. Compared to the single-source domain with single-target domain scenario, the UDA from single-source domain to multitarget domain is more challenging since it needs to consider not only the relationships between the source and the target domains but also those among the target domains. To this end, this article proposes a kind of dictionary learning-based unsupervised multitarget domain adaptation method (DL-UMTDA). In DL-UMTDA, a common dictionary is constructed to correlate the single-source and multitarget domains, while individual dictionaries are designed to exploit the private knowledge for the target domains. Through learning the corresponding dictionary representation coefficients in the UDA process, the correlations from the source to the target domains as well as these potential relationships between the target domains can be effectively exploited. In addition, we design an alternating algorithm to solve the DL-UMTDA model with theoretical convergence guarantee. Finally, extensive experiments on benchmark (Office + Caltech) and real datasets (AgeDB, Morph, and CACD) validate the superiority of the proposed method.
关键词	Dictionary learning domain correlations multitarget domain adaptation representation learning unsupervised domain adaptation (UDA)
DOI	10.1109/TNNLS.2022.3193289
关键词[WOS]	RECOGNITION ; KERNEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62176128] ; Open Projects Program of State Key Laboratory for Novel Software Technology of Nanjing University[KFKT2022B06] ; Fundamental Research Funds for the Central Universities[NJ2022028] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund ; Qing Lan Project
项目资助者	National Natural Science Foundation of China ; Open Projects Program of State Key Laboratory for Novel Software Technology of Nanjing University ; Fundamental Research Funds for the Central Universities ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund ; Qing Lan Project
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000833051700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49816
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Chen, Songcan
作者单位	1.Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China 2.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China 3.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 5.Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Peoples R China
推荐引用方式 GB/T 7714	Tian, Qing,Ma, Chuang,Cao, Meng,et al. Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:14.
APA	Tian, Qing,Ma, Chuang,Cao, Meng,Wan, Jun,Lei, Zhen,&Chen, Songcan.(2022).Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA	Tian, Qing,et al."Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):14.