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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Joint category-level and discriminative feature learning networks for unsupervised domain adaptation
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Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

机译：无监督域适应的联合类别和鉴别特征学习网络

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摘要

Unsupervised domain adaptation (UDA) aims to build a classifier for the unlabeled target domain by transferring knowledge from a well-labeled source domain. Recently deep domain adaptation methods can not effectively integrate discriminability with transferability of features, and these methods can only reduce, but not remove, the cross-domain discrepancy. To this end, this paper proposes a new domain adaptation method called Joint Category-Level and Discriminative Feature Learning Network (CDN). CDN not only achieves domain adaptation by minimizing category-level distribution discrepancy between domains but also learns discriminative feature representations via maximizing inter-category distance and selecting transferability samples simultaneously. Moreover, we develop a Transferability Weighting Module (TWM), which is based on a constructed classifier, to further strengthen the discriminability of sample's features. The experimental results demonstrate that CDN can significantly decrease the cross-domain distribution inconsistency and further promote the classification performance.
机译：无监督的域适应（UDA）旨在通过将知识从标记为标记的源域传输知识来构建未标记的目标域的分类器。最近深域适应方法不能有效地将辨别性与功能的可转换性相容，这些方法只能减少，但不删除跨域差异。为此，本文提出了一种称为联合类别级和鉴别特征学习网络（CDN）的新域适应方法。 CDN不仅通过最小化域之间的类别级分布差异来实现域适应，而且还通过最大化类别间距离和同时选择可转换性样本来学习判别特征表示。此外，我们开发了一种可转移性加权模块（TWM），其基于构造的分类器，以进一步增强样本特征的可判断性。实验结果表明，CDN可以显着降低跨域分布不一致，进一步促进分类性能。

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