Transfer Joint Matching for Unsupervised Domain Adaptation

Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem. Most prior works have explored two learning strategies independently for domain adaptation: featu...

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Bibliographic Details
Published in2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1410 - 1417
Main Authors Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Yu, Philip S.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Adaptation
Computer vision
Conferences
distribution matching
Equations
Feature extraction
feature learning
Invariants
Joints
Kernel
Matching
Optimization
Pattern recognition
Principal component analysis
Strategy
Transfer learning
Visual
Visualization
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Summary:Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem. Most prior works have explored two learning strategies independently for domain adaptation: feature matching and instance reweighting. In this paper, we show that both strategies are important and inevitable when the domain difference is substantially large. We therefore put forward a novel Transfer Joint Matching (TJM) approach to model them in a unified optimization problem. Specifically, TJM aims to reduce the domain difference by jointly matching the features and reweighting the instances across domains in a principled dimensionality reduction procedure, and construct new feature representation that is invariant to both the distribution difference and the irrelevant instances. Comprehensive experimental results verify that TJM can significantly outperform competitive methods for cross-domain image recognition problems.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.183