Knowledge Commons of Institute of Automation,CAS
Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation | |
Li, Jinfeng1; Liu, Weifeng1; Zhou, Yicong2; Yu, Jun3; Tao, Dapeng4; Xu, Changsheng5 | |
发表期刊 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS |
ISSN | 1551-6857 |
2022-08-01 | |
卷号 | 18期号:3页码:18 |
通讯作者 | Li, Jinfeng(lijinfeng_stu@163.com) |
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. |
关键词 | Domain adaptation domain-invariant graph the Nystrom method few labeled source samples |
DOI | 10.1145/3487194 |
关键词[WOS] | FRAMEWORK ; FEATURES ; KERNEL ; REGULARIZATION ; MATRIX |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Natural Science Foundation of China ; Major Scientific and Technological Projects of CNPC ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
WOS记录号 | WOS:000772650600006 |
出版者 | ASSOC COMPUTING MACHINERY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48202 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Li, Jinfeng |
作者单位 | 1.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 |
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. |
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