Volume 29 Issue 6
Dec.  2020
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ZHANG Yun, WANG Nianbin, CAI Shaobin. Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation[J]. Chinese Journal of Electronics, 2020, 29(6): 1119-1125. doi: 10.1049/cje.2020.09.013
Citation: ZHANG Yun, WANG Nianbin, CAI Shaobin. Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation[J]. Chinese Journal of Electronics, 2020, 29(6): 1119-1125. doi: 10.1049/cje.2020.09.013

Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation

doi: 10.1049/cje.2020.09.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61772152), the National Key Research and Development Program of China (No.2018YFC0806800), the Technical Basic Research Project (No.JSQB2017206C002), the Project funded by China Postdoctoral Science Foundation (No.2019M651262), the Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education of China (No.20YJCZH172), and the Postdoctoral Foundation of Heilongjiang Province (No.LBH-Z19015).
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  • Corresponding author: WANG Nianbin (corresponding author) was born in 1967. He received the Ph.D. degree in computer science and technology from Harbin Institute of Technology. He is currently a professor with the College of Computer Science and Technology, Harbin Engineering University. He is a member of China Computer Federation (CCF). His main research interests include dataspace, deep learning, and data integration. (Email:wangnianbin@hrbeu.edu.cn)
  • Received Date: 2019-10-30
  • Publish Date: 2020-12-25
  • A classifier trained on the label-rich source dataset tends to perform poorly on the unlabeled target dataset because of the distribution discrepancy across different datasets. Unsupervised domain adaptation aims to transfer knowledge from the labeled source dataset to the unlabeled target dataset to solve this problem. Most of the existing unsupervised domain adaptation methods only concentrate on learning domain-invariant features across different domains, but they neglect the discriminability of the learned features to satisfy the cluster assumption. In this paper, we propose Semantic pairwise centroid alignment (SPCA), which is a point-wise method to learn both domain-invariant and discriminative features for homogeneous unsupervised domain adaptation. SPCA utilizes a novel semantic centroid loss to reduce the intraclass distance in feature space by using source data and target High-confidence centroid points (HCCPs). Then a classifier trained on source features is expected to generalize well on target features. Extensive experiments on visual recognition tasks verify the effectiveness of the proposed SPCA and also demonstrate that both domaininvariant and discriminative features learned by SPCA can significantly boost the performance of homogeneous unsupervised domain adaptation.
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