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Graph Matching and Pseudo-Label Guided Deep Unsupervised Domain Adaptation

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

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Abstract

The goal of domain adaptation is to train a high-performance predictive model on the target domain data by using knowledge from the source domain data, which has different but related data distribution. In this paper, we consider unsupervised domain adaptation where we have labelled source domain data but unlabelled target domain data. Our solution to unsupervised domain adaptation is to learn a domain-invariant representation that is also category discriminative. Domain-invariant representations are realized by minimizing the domain discrepancy. To minimize the domain discrepancy, we propose a novel graph-matching metric between the source and target domain representations. Minimizing this metric allows the source and target representations to be in support of each other. We further exploit confident unlabelled target domain samples and their pseudo-labels to refine our proposed model. We expect the refining step to improve the performance further. This is validated by performing experiments on standard image classification adaptation datasets. Results showed our proposed approach out-perform previous domain-invariant representation learning approaches.

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Acknowledgments

This work was supported in part by the National Science Foundation under Grant IIS-1813935. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Debasmit Das & C. S. George Lee

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  1. Debasmit Das
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  2. C. S. George Lee
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Corresponding author

Correspondence to Debasmit Das .

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Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Das, D., Lee, C.S.G. (2018). Graph Matching and Pseudo-Label Guided Deep Unsupervised Domain Adaptation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_34

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  • DOI: https://doi.org/10.1007/978-3-030-01424-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

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