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Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning | |
Tian, Lei1,2; Tang, Yongqiang1; Hu, Liangchen3; Ren, Zhida1,2; Zhang, Wensheng1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2020 | |
卷号 | 29页码:9703-9718 |
摘要 | 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. |
关键词 | Domain adaptation class centroid matching local manifold self-learning |
DOI | 10.1109/TIP.2020.3031220 |
关键词[WOS] | KERNEL |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Key-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] |
项目资助者 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000583696200003 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 机器学习 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41753 |
专题 | 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 |
通讯作者 | Zhang, Wensheng |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Domain Adaptation by(3443KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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