Attention-Based Multi-Source Domain Adaptation

doi:10.1109/TIP.2021.3065254

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Attention-Based Multi-Source Domain Adaptation
	Zuo, Yukun 3; Yao, Hantao 2; Xu, Changsheng 1,2
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2021
卷号	30 页码:3793-3803
关键词	Correlation Adaptation models Feature extraction Target recognition Data models Transfer learning Visualization Multi-source domain adaptation attention-based multi-source domain adaptation weighted moment distance
ISSN号	1057-7149
DOI	10.1109/TIP.2021.3065254
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要	Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source domains to one target domain. Inspired by single-source domain adaptation, existing methods solve MSDA by aligning the data distributions between the target domain and each source domain. However, aligning the target domain with the dissimilar source domain would harm the representation learning. To address the above issue, an intuitive motivation of MSDA is using the attention mechanism to enhance the positive effects of the similar domains, and suppress the negative effects of the dissimilar domains. Therefore, we propose Attention-Based Multi-Source Domain Adaptation (ABMSDA) by considering the domain correlations to alleviate the effects caused by dissimilar domains. To obtain the domain correlations between source and target domains, ABMSDA firstly trains a domain recognition model to calculate the probability that the target images belong to each source domain. Based on the domain correlations, Weighted Moment Distance (WMD) is proposed to pay more attention on the source domains with higher similarities. Furthermore, Attentive Classification Loss (ACL) is developed to constrain that the feature extractor can generate the alignment and discriminative visual representations. The evaluations on two benchmarks demonstrate the effectiveness of the proposed model, e.g., an average of 6.1% improvement on the challenging DomainNet dataset.
资助项目	National Key Research and Development Program of China[2018AAA0102205] ; National Natural Science Foundation of China[61902399] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61720106006] ; Beijing Natural Science Foundation[L201001] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000633391800005
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Research Program of Frontier Sciences, CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44170]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者	Xu, Changsheng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Zuo, Yukun,Yao, Hantao,Xu, Changsheng. Attention-Based Multi-Source Domain Adaptation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:3793-3803.
APA	Zuo, Yukun,Yao, Hantao,&Xu, Changsheng.(2021).Attention-Based Multi-Source Domain Adaptation.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,3793-3803.
MLA	Zuo, Yukun,et al."Attention-Based Multi-Source Domain Adaptation".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):3793-3803.