Add to ORKGA novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem is introduced. 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 pr...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
A novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem...
This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised d...
This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised d...
As a fundamental constituent of machine learning, domain adaptation generalizes a learning model fro...
The key question in transfer learning (TL) research is how to make model induction transferable acro...
Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by le...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Learning an appropriate feature representation across source and target domains is one of the most e...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the a...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the a...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Kernel Alignment has been developed and analysed in the field of multiple kernel learning in the pas...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
A novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem...
This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised d...
This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised d...
As a fundamental constituent of machine learning, domain adaptation generalizes a learning model fro...
The key question in transfer learning (TL) research is how to make model induction transferable acro...
Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by le...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Learning an appropriate feature representation across source and target domains is one of the most e...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the a...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the a...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Kernel Alignment has been developed and analysed in the field of multiple kernel learning in the pas...
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation prob...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...