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Rotation Regularization Without Rotation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13685))

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Abstract

In various visual classification tasks, we enjoy significant performance improvement by deep convolutional neural networks (CNNs). To further boost performance, it is effective to regularize feature representation learning of CNNs such as by considering margin to improve feature distribution across classes. In this paper, we propose a regularization method based on random rotation of feature vectors. Random rotation is derived from cone representation to describe angular margin of a sample. While it induces geometric regularization to randomly rotate vectors by means of rotation matrices, we theoretically formulate the regularization in a statistical form which excludes costly geometric rotation as well as effectively imposes rotation-based regularization on classification in training CNNs. In the experiments on classification tasks, the method is thoroughly evaluated from various aspects, while producing favorable performance compared to the other regularization methods. Codes are available at https://github.com/tk1980/StatRot.

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Notes

  1. 1.

    For odd d, we apply \((d-1)/2\) with \(\boldsymbol{V}\in \Re ^{d\times d-1}\).

  2. 2.

    ResNet10 produces \(d=512\)-dimensional features for \(C=1000\) ImageNet classes.

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Author information

Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Tsukuba, 305 -8560, Japan

    Takumi Kobayashi

  2. University of Tsukuba, Tsukuba, 305-8577, Japan

    Takumi Kobayashi

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  1. Takumi Kobayashi
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Correspondence to Takumi Kobayashi .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Kobayashi, T. (2022). Rotation Regularization Without Rotation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13685. Springer, Cham. https://doi.org/10.1007/978-3-031-19806-9_37

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  • DOI: https://doi.org/10.1007/978-3-031-19806-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19805-2

  • Online ISBN: 978-3-031-19806-9

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