Abstract
In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions become similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that probability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the corresponding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of-the-art results on a standard object recognition benchmark.
Original language | English |
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Title of host publication | Proceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition |
Editors | Ronen Basri, Cornelia Fermuller, Aleix Martinez, René Vidal |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 2481-2488 |
Number of pages | 8 |
ISBN (Electronic) | 9781479951178 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2014 - Columbus, United States of America Duration: 23 Jun 2014 → 28 Jun 2014 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6909096 (IEEE Conference Proceedings) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2014 |
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Abbreviated title | CVPR 2014 |
Country/Territory | United States of America |
City | Columbus |
Period | 23/06/14 → 28/06/14 |
Internet address |
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Keywords
- Domain Adaptation
- Object Recognition
- Statistical Manifold