CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation
Li, Jinfeng1; Liu, Weifeng1; Zhou, Yicong2; Yu, Jun3; Tao, Dapeng4; Xu, Changsheng5
Source PublicationACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN1551-6857
2022-08-01
Volume18Issue:3Pages:18
Corresponding AuthorLi, Jinfeng(lijinfeng_stu@163.com)
AbstractDomain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. However, these algorithms will be infeasible when only a few labeled data exist in the source domain, thus the performance decreases significantly. To address this challenge, we propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples. Firstly, DGL introduces the Nystrom method to construct a plastic graph that shares similar geometric property with the target domain. Then, DGL flexibly employs the Nystrom approximation error to measure the divergence between the plastic graph and source graph to formalize the distribution mismatch from the geometric perspective. Through minimizing the approximation error, DGL learns a domain-invariant geometric graph to bridge the source and target domains. Finally, we integrate the learned domain-invariant graph with the semi-supervised learning and further propose an adaptive semi-supervised model to handle the cross-domain problems. The results of extensive experiments on popular datasets verify the superiority of DGL, especially when only a few labeled source samples are available.
KeywordDomain adaptation domain-invariant graph the Nystrom method few labeled source samples
DOI10.1145/3487194
WOS KeywordFRAMEWORK ; FEATURES ; KERNEL ; REGULARIZATION ; MATRIX
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61671480] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[62020106007] ; Major Scientific and Technological Projects of CNPC[ZD2019-183-008] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[202000009]
Funding OrganizationNational Natural Science Foundation of China ; Major Scientific and Technological Projects of CNPC ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000772650600006
PublisherASSOC COMPUTING MACHINERY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48202
Collection模式识别国家重点实验室_多媒体计算
Corresponding AuthorLi, Jinfeng
Affiliation1.Xidian Univ, China Univ Petr East China, State Key Lab Integrated Serv Networks, 66 Changjiang West Rd, Qingdao 266580, Peoples R China
2.Univ Macau, Macau, Peoples R China
3.Hangzhou Dianzi Univ, 1158 2 Dajie, Hangzhou 310018, Peoples R China
4.Yunnan Univ, Kunming 650091, Yunnan, Peoples R China
5.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Jinfeng,Liu, Weifeng,Zhou, Yicong,et al. Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2022,18(3):18.
APA Li, Jinfeng,Liu, Weifeng,Zhou, Yicong,Yu, Jun,Tao, Dapeng,&Xu, Changsheng.(2022).Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,18(3),18.
MLA Li, Jinfeng,et al."Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18.3(2022):18.
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