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Reliable Domain Adaptation with Classifiers Competition

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11936))

Abstract

Unsupervised domain adaptation (UDA) aims to transfer labeled source domain knowledge to the unlabeled target domain. Previous methods usually solve it by minimizing joint distribution divergence and obtaining the pseudo target labels via source classifier. However, those methods ignore that the source classifier always misclassifies partial target data and the prediction bias seriously deteriorates adaptation performance. It remains an open issue but ubiquitous in UDA, and to alleviate this issue, a Reliable Domain Adaptation (RDA) method is proposed in this paper. Specifically, we propose double task-classifiers and dual domain-specific projections to align those easily misclassified and unreliable target samples into reliable ones in an adversarial manner. In addition, the domain shift of both manifold and category space is reduced in the projection learning step. Extensive experiments on various databases demonstrate the superiority of RDA over state-of-the-art unsupervised domain adaptation methods.

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Authors and Affiliations

  1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China

    Jingru Fu & Lei Zhang

Authors
  1. Jingru Fu
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  2. Lei Zhang
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Corresponding author

Correspondence to Lei Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Fu, J., Zhang, L. (2019). Reliable Domain Adaptation with Classifiers Competition. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-36204-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36203-4

  • Online ISBN: 978-3-030-36204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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