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Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation
Jian Liang1,2; Ran He1,2,3; Zhenan Sun1,2,3; Tieniu Tan1,2,3
发表期刊IEEE Trans. Pattern Anal. Machine Intell.
2019
卷号41期号:5页码:1027-1042
文章类型regular paper
摘要

    Unsupervised domain adaptation aims to leverage the labeled source data to learn with the unlabeled target data. Previous trandusctive methods tackle it by iteratively seeking a low-dimensional projection to extract the invariant features and obtaining the pseudo target labels via building a classifier on source data. However, they merely concentrate on minimizing the cross-domain distribution divergence, while ignoring the intra-domain structure especially for the target domain. Even after projection, possible risk factors like imbalanced data distribution may still hinder the performance of target label inference. In this paper, we propose a simple yet effective domain-invariant projection ensemble approach to tackle these two issues together. Specifically, we seek the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains. To further enhance the target label inference, we first develop a `sampling-and-fusion' framework, under which multiple projections are independently learned based on various randomized coupled domain subsets. Subsequently, aggregating models such as majority voting are utilized to leverage multiple projections and classify unlabeled target data. 
    Extensive experimental results on six visual benchmarks including object, face, and digit images, demonstrate that the proposed methods gain remarkable margins over state-of-the-art unsupervised domain adaptation methods. 

关键词Unsupervised Domain Adaptation Domain-invariant Projection Class-clustering Sampling-and-fusion
收录类别SCI ; SCIE ; SSCI ; EI
语种英语
WOS记录号WOS:000463607400001
七大方向——子方向分类模式识别基础
引用统计
被引频次:85[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/23803
专题模式识别实验室
通讯作者Zhenan Sun; Tieniu Tan
作者单位1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA)
2.University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Jian Liang,Ran He,Zhenan Sun,et al. Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation[J]. IEEE Trans. Pattern Anal. Machine Intell.,2019,41(5):1027-1042.
APA Jian Liang,Ran He,Zhenan Sun,&Tieniu Tan.(2019).Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation.IEEE Trans. Pattern Anal. Machine Intell.,41(5),1027-1042.
MLA Jian Liang,et al."Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation".IEEE Trans. Pattern Anal. Machine Intell. 41.5(2019):1027-1042.
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