首页|A Two-Way alignment approach for unsupervised multi-Source domain adaptation
A Two-Way alignment approach for unsupervised multi-Source domain adaptation
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Domain adaptation aims at transferring knowledge from labeled source domain to unlabeled target do -main. Current advances primarily concern single source domain and neglect the setting of multiple source domains. Previous unsupervised multi-source domain adaptation (MDA) algorithms only consider domain-level alignment, while neglecting the category-level information among multiple domains and the instance variations inside each domain. This paper introduces a Two-Way alignment framework for MDA (TWMDA), which considers both domain-level and category-level alignments, and addresses the in-stance variations. We first align the target and multiple sources on the domain-level by an adversarial learning process. To circumvent the drawbacks of adversarial learning, we further reduce the domain gap on the category-level by minimizing the distance between the category prototypes and unlabeled tar -get instances. To address the instance variations, we design an instance weighting strategy for diverse source instances. The effectiveness of TWMDA is demonstrated on three benchmark datasets for image classification. (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
