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Transfer Learning with Dynamic Distribution Adaptation

文献类型：期刊论文

作者Wang, Jindong1; Chen, Yiqiang2; Feng, Wenjie2; Yu, Han3; Huang, Meiyu4; Yang, Qiang5
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2020-02-01
卷号11期号:1页码:25
关键词Transfer learning domain adaptation distribution alignment deep learning subspace learning kernel method
ISSN号2157-6904
DOI10.1145/3360309
英文摘要Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this article, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) ManifoldDynamic DistributionAdaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and set up a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance, which leads to better performance. We believe this observation can be helpful for future research in transfer learning.
资助项目National Key R&D Program of China[2016YFB1001200] ; NSFC[61572471] ; NSFC[61972383] ; Hong Kong CERG projects[16209715] ; Hong Kong CERG projects[16244616] ; Nanyang Technological University, Nanyang Assistant Professorship (NAP) ; Beijing Municipal Science & Technology Commission[Z171100000117017]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000535726400006
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/15322]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Yiqiang
作者单位1.Microsoft Res Asia, 5 Danling St, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
3.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore
4.China Acad Space Technol, Qian Xuesen Lab Space Technol, Beijing, Peoples R China
5.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jindong,Chen, Yiqiang,Feng, Wenjie,et al. Transfer Learning with Dynamic Distribution Adaptation[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2020,11(1):25.
APA Wang, Jindong,Chen, Yiqiang,Feng, Wenjie,Yu, Han,Huang, Meiyu,&Yang, Qiang.(2020).Transfer Learning with Dynamic Distribution Adaptation.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,11(1),25.
MLA Wang, Jindong,et al."Transfer Learning with Dynamic Distribution Adaptation".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 11.1(2020):25.

入库方式： OAI收割

来源：计算技术研究所

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