Abstract. A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains.
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Abstract. In this paper, we present an efficient Multi-class Heterogeneous Domain Adaptation (HDA) method, where data from the source and target domains are ...
A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature map- ping between different types of features across domains.
A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature map- ping between different types of features across domains.
Heterogeneous domain adaptation aims to exploit the source domain data to train a prediction model for the target domain with different input feature space.
An efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of ...
Abstract. In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are repre-sented ...
In this paper, we propose a novel semi-supervised subspace co-projection method to address multi-class heterogeneous domain adaptation. The proposed method ...
Aug 6, 2020 · In this article, we study the multisource HDA problem and propose a conditional weighting adversarial network (CWAN) to address it. The proposed ...
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Apr 1, 2019 · Multi-class Heterogeneous Domain Adaptation ; Publication Type: Journal Article ; Citation: Journal of Machine Learning Research, 2019, 20 ; Issue ...