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Cross-domain neural-kernel networks
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DOI：10.1016/j.patrec.2019.06.007.png)
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This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem. The proposed model consists of two stream neural-kernel networks corresponding to the source and target domains which are enriched with a coupling term. Each stream neural-kernel networks follows a combination of neural network layer and an explicit feature map constructed by means of random Fourier features. The introduced coupling term aims at enforcing correlations among the output of the intermediate layers of the two stream networks as well as encouraging the two networks to learn shared representation of the data from both source and target domains. Experimental results are given to illustrate the effectiveness of the proposed approaches on synthetic and real-life datasets. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Domain adaptation
Neural networks
Kernel methods
Coupling regularization
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