Abstract
This paper introduces a learning scheme to construct a Hilbert space (i.e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the Mahalanobis metric of the latent space to simultaneously minimizing a notion of domain variance while maximizing a measure of discriminatory power. In particular, we make use of the Riemannian optimization techniques to match statistical properties (e.g., first and second order statistics) between samples projected into the latent space from different domains. Upon availability of class labels, we further deem samples sharing the same label to form more compact clusters while pulling away samples coming from different classes. We extensively evaluate and contrast our proposal against state-of-the-art methods for the task of visual domain adaptation using both handcrafted and deep-net features. Our experiments show that even with a simple nearest neighbor classifier, the proposed method can outperform several state-of-the-art methods benefiting from more involved classification schemes.
Original language | English |
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Title of host publication | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) |
Editors | Jim Rehg, Yanxi Liu, Ying Wu, Camillo Taylor |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 3956-3965 |
Number of pages | 10 |
ISBN (Electronic) | 9781538604571 |
ISBN (Print) | 9781538604588 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America Duration: 21 Jul 2017 → 26 Jul 2017 http://cvpr2017.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/8097368/proceeding (Proceedings) |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2017 |
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Abbreviated title | CVPR 2017 |
Country/Territory | United States of America |
City | Honolulu |
Period | 21/07/17 → 26/07/17 |
Internet address |