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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 768–784Cite as

Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12369))

Abstract

Unsupervised image classification is a challenging computer vision task. Deep learning-based algorithms have achieved superb results, where the latest approach adopts unified losses from embedding and class assignment processes. Since these processes inherently have different goals, jointly optimizing them may lead to a suboptimal solution. To address this limitation, we propose a novel two-stage algorithm in which an embedding module for pretraining precedes a refining module that concurrently performs embedding and class assignment. Our model outperforms SOTA when tested with multiple datasets, by substantially high accuracy of 81.0% for the CIFAR-10 dataset (i.e., increased by 19.3 percent points), 35.3% accuracy for CIFAR-100-20 (9.6 pp) and 66.5% accuracy for STL-10 (6.9 pp) in unsupervised tasks.

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Notes

  1. 1.

    Codes released at https://github.com/dscig/TwoStageUC.

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Acknowledgement

We thank Cheng-Te Li and Yizhan Xu for their insights and discussions. This work was supported by the Institute for Basic Science (IBS-R029-C2) and the Basic Science Research Program through the National Research Foundation funded by the Ministry of Science and ICT in Korea (No. NRF-2017R1E1A1A01076400).

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Sungwon Han, Sungwon Park, Sungkyu Park & Meeyoung Cha

  2. Data Science Group, Institute for Basic Science, Daejeon, South Korea

    Sundong Kim & Meeyoung Cha

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  1. Sungwon Han
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  2. Sungwon Park
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  3. Sungkyu Park
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  5. Meeyoung Cha
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Corresponding author

Correspondence to Meeyoung Cha .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Han, S., Park, S., Park, S., Kim, S., Cha, M. (2020). Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_45

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  • DOI: https://doi.org/10.1007/978-3-030-58586-0_45

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