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Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12458))

Abstract

Unsupervised Domain Adaptation (UDA) attempts to transfer knowledge from a labeled source domain to an unlabeled target domain. Recently, domain-adversarial learning has become an increasingly popular method to tackle this task, which bridges source domain and target domain by adversarially learning domain-invariant representations that cannot be discriminated by a domain discriminator. In spite of the great success achieved by domain-adversarial learning, most of existing methods still suffer two major limitations: (1) due to focusing only on learning domain-invariant representations, they ignore the individual characteristics of each domain and fail to extract domain-specific information that is beneficial for final classification; (2) by focusing only on performing domain-level distribution alignment to learn domain–invariant representations, they fail to achieve the invariance of representations at a class level, which may lead to incorrect distribution alignment. To address the above issues, we propose in this paper a novel model called Joint Domain-Adversarial Reconstruction Network (JDARN), which integrates domain-adversarial learning with data reconstruction to learn both domain–invariant and domain-specific representations. Meanwhile, we propose to employ two novel discriminators called joint domain-class discriminators to achieve the joint alignment and adopt a novel joint adversarial loss to train them. With both domain and class information of two domains, the two discriminators can be used to promote domain-invariant representation learning towards the class level, not only the domain level. Extensive experimental results reveal that the proposed JDARN exceeds the state-of-the-art performance on two standard UDA datasets.

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Notes

  1. 1.

    https://www.imageclef.org/2014/adaptation.

  2. 2.

    Codes are available at https://github.com/NaivePawn/JDARN.

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Acknowledgements

This paper is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1403400), the National Natural Science Foundation of China (Grant No. 61876080), the Key Research and Development Program of Jiangsu (Grant No. BE2019105), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Qian Chen, Yuntao Du, Zhiwen Tan, Yi Zhang & Chongjun Wang

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  1. Qian Chen
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  2. Yuntao Du
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  3. Zhiwen Tan
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  4. Yi Zhang
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Correspondence to Chongjun Wang .

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Editors and Affiliations

  1. Albert-Ludwigs-Universität, Freiburg, Germany

    Frank Hutter

  2. TU Darmstadt, Darmstadt, Germany

    Kristian Kersting

  3. Ghent University, Ghent, Belgium

    Jefrey Lijffijt

  4. Saarland University, SaarbrĂĽcken, Germany

    Isabel Valera

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Chen, Q., Du, Y., Tan, Z., Zhang, Y., Wang, C. (2021). Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12458. Springer, Cham. https://doi.org/10.1007/978-3-030-67661-2_38

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  • DOI: https://doi.org/10.1007/978-3-030-67661-2_38

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