中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

文献类型:期刊论文

作者Li, Jinfeng5; Liu, Weifeng5; Zhou, Yicong1; Yu, Jun2; Tao, Dapeng3; Xu, Changsheng4
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2022-08-01
卷号18期号:3页码:18
ISSN号1551-6857
关键词Domain adaptation domain-invariant graph the Nystrom method few labeled source samples
DOI10.1145/3487194
通讯作者Li, Jinfeng(lijinfeng_stu@163.com)
英文摘要Domain 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.
WOS关键词FRAMEWORK ; FEATURES ; KERNEL ; REGULARIZATION ; MATRIX
资助项目National 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]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:000772650600006
资助机构National Natural Science Foundation of China ; Major Scientific and Technological Projects of CNPC ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
源URL[http://ir.ia.ac.cn/handle/173211/48201]  
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Li, Jinfeng
作者单位1.Univ Macau, Macau, Peoples R China
2.Hangzhou Dianzi Univ, 1158 2 Dajie, Hangzhou 310018, Peoples R China
3.Yunnan Univ, Kunming 650091, Yunnan, Peoples R China
4.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
5.Xidian Univ, China Univ Petr East China, State Key Lab Integrated Serv Networks, 66 Changjiang West Rd, Qingdao 266580, Peoples R China
推荐引用方式
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.

入库方式: OAI收割

来源:自动化研究所

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