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Unsupervised multi-source domain adaptation with graph convolution network and multi-alignment in mixed latent space

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Abstract

This paper proposes an unsupervised multi-source domain adaptation algorithm with graph convolution network and multi-alignment in mixed latent space, which leverages domain labels, data structure, and category labels in a unified network but improves domain-invariant semantic representation by several innovations. Specifically, a novel data structure alignment is proposed to exploit the inherent properties of different domains while using current domain alignment and classification result alignment. Through this design, category consistency can be considered in both latent space, and domain and structure discrepancy between different source domains and the target domain can be eliminated. Moreover, we also use category alignment based on both CNN and GCN features to optimize category decision boundary. Experiment results show that the proposed method brings sufficient improvement especially for adaptation tasks with large shift in data distribution.

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Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Dong Chen, Hongqing Zhu & Yiwen Dai

  2. Department of Mathematics, Natural, Mathematical and Engineering Sciences, King’s College London, Strand, London, WC2R 2LS, UK

    Suyi Yang

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  1. Dong Chen
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  2. Hongqing Zhu
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  3. Suyi Yang
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  4. Yiwen Dai
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Correspondence to Hongqing Zhu.

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This work was supported by the National Nature Science Foundation of China under Grant 61872143.

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Chen, D., Zhu, H., Yang, S. et al. Unsupervised multi-source domain adaptation with graph convolution network and multi-alignment in mixed latent space. SIViP 17, 855–863 (2023). https://doi.org/10.1007/s11760-022-02298-w

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  • DOI: https://doi.org/10.1007/s11760-022-02298-w

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