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Dual-aligned unsupervised domain adaptation with graph convolutional networks

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Abstract

In recent years, graph convolutional networks have achieved great success in unsupervised domain adaptation task. Although these works make effort to reduce the distribution difference between domains, they do not take into account the issue of distribution difference reduction in the class level. In this paper, we propose a Dual-aligned Unsupervised Domain Adaptation with Graph Convolutional Networks (DUDA-GCN) framework to align domain distributions and the distributions of two domains corresponding to each category jointly. The framework contains two parts, i.e., a cross-domain feature extractor and a dual aligner of distribution. The former adopts a two-channel sub-network, with each channel to fully explore the relation among within-domain samples, based on GCN with shared weights to learn common feature representations of two domains. The dual aligner contains an adversarial domain discriminator and a category aligner, where the domain discriminator is designed to reduce the distribution difference across domains. A pseudo-label generator is designed to generate pseudo-labels for unlabeled samples. With the generated pseudo-labels of unlabeled samples and the real labels of labeled samples, the category aligner aligns the sample distributions across domains of the same category. Extensive empirical evaluation on three real-world datasets shows that DUDA-GCN can perform better than state-of-the-art related domain adaptation methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 62076139, 61702280, 62176069), Zhejiang Lab (No. 2021KF0AB05), the National Postdoctoral Program for Innovative Talents (No. BX20180146), China Postdoctoral Science Foundation (No. 2019M661901), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 2019K024), Future Network Scientific Research Fund Project (No. FNSRFP-2021-YB-15), 1311 Talent Program of Nanjing University of Posts and Telecommunications, and Natural Science Foundation of Jiangsu Province (No. BK20170900).

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Authors and Affiliations

  1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

    Fei Wu, Pengfei Wei & Chang-Hui Hu

  2. Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan

    Guangwei Gao

  3. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Qi Ge

  4. School of Computer, Wuhan University, Wuhan, China

    Xiao-Yuan Jing

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  1. Fei Wu
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  4. Chang-Hui Hu
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  5. Qi Ge
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Correspondence to Fei Wu.

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Wu, F., Wei, P., Gao, G. et al. Dual-aligned unsupervised domain adaptation with graph convolutional networks. Multimed Tools Appl 81, 14979–14997 (2022). https://doi.org/10.1007/s11042-022-12379-0

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  • DOI: https://doi.org/10.1007/s11042-022-12379-0

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