Transfer Feature Learning with Joint Distribution Adaptation

Mingsheng Long; Jianmin Wang; Guiguang Ding; Jiaguang Sun; Philip S. Yu

doi:10.1109/iccv.2013.274

Abstract

Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.

Keywords

Computer scienceMarginal distributionJoint probability distributionTransfer of learningArtificial intelligenceClassifier (UML)Conditional probability distributionDimensionality reductionDomain adaptationPattern recognition (psychology)Feature (linguistics)Curse of dimensionalityFeature learningDomain (mathematical analysis)Feature vectorMachine learningMathematicsStatistics

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Publication Info

Year: 2013
Type: article
Pages: 2200-2207
Citations: 1984
Access: Closed

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APA Style

                            
                                    Mingsheng Long, 
                                
                                    Jianmin Wang, 
                                
                                    Guiguang Ding
                                
                                et al.
                            
                            (2013). 
                            Transfer Feature Learning with Joint Distribution Adaptation. 
                            
                            , 2200-2207.
                            https://doi.org/10.1109/iccv.2013.274

Identifiers

DOI: 10.1109/iccv.2013.274