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Out-of-Distribution Detection Without Class Labels

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13802))

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Abstract

Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consist of multiple semantic classes (e.g. multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection without class labels. Our simple but effective solution consists of two stages: we first discover “pseudo-class” labels using unsupervised clustering. Then using these pseudo-class labels, we are able to use standard supervised out-of-distribution detection methods. We verify the performance of our method by favorable comparison to the state-of-the-art, and provide extensive analysis and ablations.

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Notes

  1. 1.

    https://github.com/alinlab/CSI.

  2. 2.

    https://github.com/wvangansbeke/Unsupervised-Classification.

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Acknowledgements

This work was partly supported by the Malvina and Solomon Pollack scholarship and, the Federmann Cyber Security Research Center in conjunction with the Israel National Cyber Directorate.

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Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Niv Cohen, Ron Abutbul & Yedid Hoshen

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  1. Niv Cohen
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  2. Ron Abutbul
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  3. Yedid Hoshen
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Corresponding author

Correspondence to Niv Cohen .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Cohen, N., Abutbul, R., Hoshen, Y. (2023). Out-of-Distribution Detection Without Class Labels. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13802. Springer, Cham. https://doi.org/10.1007/978-3-031-25063-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-25063-7_7

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