Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
Tian, Lei1,2; Tang, Yongqiang1; Hu, Liangchen3; Ren, Zhida1,2; Zhang, Wensheng1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2020
Volume29Pages:9703-9718
Abstract

Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the distribution discrepancy between two domains in a proper way such that they can be treated indifferently for learning. In this paper, we propose a novel domain adaptation approach, which can thoroughly explore the data distribution structure of target domain. Specifically, we regard the samples within the same cluster in target domain as a whole rather than individuals and assigns pseudo-labels to the target cluster by class centroid matching. Besides, to exploit the manifold structure information of target data more thoroughly, we further introduce a local manifold self-learning strategy into our proposal to adaptively capture the inherent local connectivity of target samples. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal with theoretical convergence guarantee. In addition to unsupervised domain adaptation, we further extend our method to the semi-supervised scenario including both homogeneous and heterogeneous settings in a direct but elegant way. Extensive experiments on seven benchmark datasets validate the significant superiority of our proposal in both unsupervised and semi-supervised manners.

KeywordDomain adaptation class centroid matching local manifold self-learning
DOI10.1109/TIP.2020.3031220
WOS KeywordKERNEL
Indexed BySCI
Language英语
Funding ProjectKey-Area Research and Development Program of Guangdong Province[2019B010153002] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61772525]
Funding OrganizationKey-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000583696200003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification机器学习
Citation statistics
Cited Times:45[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41753
Collection多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队
Corresponding AuthorZhang, Wensheng
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Nanjing Univ Sci & Technol, Nanjing 210094, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tian, Lei,Tang, Yongqiang,Hu, Liangchen,et al. Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:9703-9718.
APA Tian, Lei,Tang, Yongqiang,Hu, Liangchen,Ren, Zhida,&Zhang, Wensheng.(2020).Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,9703-9718.
MLA Tian, Lei,et al."Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):9703-9718.
Files in This Item: Download All
File Name/Size DocType Version Access License
Domain Adaptation by(3443KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tian, Lei]'s Articles
[Tang, Yongqiang]'s Articles
[Hu, Liangchen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tian, Lei]'s Articles
[Tang, Yongqiang]'s Articles
[Hu, Liangchen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tian, Lei]'s Articles
[Tang, Yongqiang]'s Articles
[Hu, Liangchen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.