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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2851067
|2 doi
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|a pubmed24n0954.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Ding, Zhengming
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Semi-supervised Deep Domain Adaptation via Coupled Neural Networks
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| 264 |
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1 |
|c 2018
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 31.07.2018
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| 500 |
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|a Date Revised 31.07.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target. In this paper, we propose a semi-supervised deep domain adaptation framework, in which the multi-layer feature extractor and a multi-class classifier are jointly learned to benefit from each other. Specifically, we develop a novel semi-supervised class-wise adaptation manner to fight off the conditional distribution mismatch between two domains by assigning a probabilistic label to each target sample, i.e., multiple class labels with different probabilities. Furthermore, a multi-class classifier is simultaneously trained on labeled source and unlabeled target samples in a semi-supervised fashion. In this way, the deep structure can formally alleviate the domain divergence and enhance the feature transferability. Experimental evaluations on several standard cross-domain benchmarks verify the superiority of our proposed approach
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Nasrabadi, Nasser M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Yun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 11 vom: 18. Nov., Seite 5214-5224
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:27
|g year:2018
|g number:11
|g day:18
|g month:11
|g pages:5214-5224
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2018.2851067
|3 Volltext
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| 952 |
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|d 27
|j 2018
|e 11
|b 18
|c 11
|h 5214-5224
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