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231224s2015 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2015.2479405
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|a eng
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1 |
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|a Nguyen, Hien V
|e verfasserin
|4 aut
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| 245 |
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|a DASH-N
|b Joint Hierarchical Domain Adaptation and Feature Learning
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|c 2015
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|a Date Completed 03.02.2016
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|a Date Revised 27.01.2016
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Complex visual data contain discriminative structures that are difficult to be fully captured by any single feature descriptor. While recent work on domain adaptation focuses on adapting a single hand-crafted feature, it is important to perform adaptation of a hierarchy of features to exploit the richness of visual data. We propose a novel framework for domain adaptation using a sparse and hierarchical network (DASH-N). Our method jointly learns a hierarchy of features together with transformations that rectify the mismatch between different domains. The building block of DASH-N is the latent sparse representation. It employs a dimensionality reduction step that can prevent the data dimension from increasing too fast as one traverses deeper into the hierarchy. The experimental results show that our method compares favorably with the competing state-of-the-art methods. In addition, it is shown that a multi-layer DASH-N performs better than a single-layer DASH-N
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Ho, Huy Tho
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| 700 |
1 |
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|a Patel, Vishal M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chellappa, Rama
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 24(2015), 12 vom: 10. Dez., Seite 5479-91
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