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International Symposium on Visual Computing

ISVC 2021: Advances in Visual Computing pp 341–352Cite as

Adaptive Feature Norm for Unsupervised Subdomain Adaptation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13017))

Abstract

In many real-world problems, obtaining labeled data for a specific machine learning task is expensive. Unsupervised Domain Adaptation (UDA) aims at learning a good predictive model for the target domain using labeled information from the source but only unlabeled samples from the target domain. Most of the previous methods tackle this issue with adversarial methods that contain several loss functions and converge slowly. Recently, subdomain adaptation, which focuses on nuances of the distribution of the relevant subdomains, is getting more and more attention in the UDA field. This paper proposes a technique that uses the adaptive feature norm with subdomain adaptation to boost the transfer gains. Subdomain adaptation can enhance the ability of deep adaptation networks by capturing the fine-grained features from each category. Additionally, we have incorporated an adaptive feature norm approach to increase transfer gains. Our method shows state-of-the-art results on the popular visual classification datasets, including Office-31, Office Home, and Image-CLEF datasets.

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Notes

  1. 1.

    http://imageclef.org/2014/adaptation.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

    Ashiq Imran & Vassilis Athitsos

Authors
  1. Ashiq Imran
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  2. Vassilis Athitsos
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Corresponding author

Correspondence to Ashiq Imran .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

  3. University of South Carolina, Columbia, SC, USA

    Tong Yan

  4. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  5. School of Engineering and Computing, University of Durham, Durham, Durham, UK

    Frederick Li

  6. Airbnb, New York, NY, USA

    Conglei Shi

  7. Peking University, Beijing, China

    Xiaoru Yuan

  8. Purdue University, West Lafayette, IN, USA

    Christos Mousas

  9. IST, School of Modeling, Simulation, and Training, Orlando, FL, USA

    Gerd Bruder

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Cite this paper

Imran, A., Athitsos, V. (2021). Adaptive Feature Norm for Unsupervised Subdomain Adaptation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13017. Springer, Cham. https://doi.org/10.1007/978-3-030-90439-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-90439-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90438-8

  • Online ISBN: 978-3-030-90439-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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