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GroSS: Group-Size Series Decomposition for Grouped Architecture Search

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12371))

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Abstract

We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks. Group-size Series (GroSS) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to enable simultaneous training of differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS is able to train an entire grouped convolution architecture search-space concurrently. We demonstrate this through architecture searches with performance objectives on multiple datasets and networks. GroSS enables more effective and efficient search for grouped convolutional architectures.

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Acknowledgements

We gratefully acknowledge the European Commission Project Multiple-actOrs Virtual Empathic CARegiver for the Elder (MoveCare) for financially supporting the authors for this work.

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Authors and Affiliations

  1. Active Vision Laboratory, University of Oxford, Oxford, UK

    Henry Howard-Jenkins, Yiwen Li & Victor Adrian Prisacariu

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  1. Henry Howard-Jenkins
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  2. Yiwen Li
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  3. Victor Adrian Prisacariu
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Corresponding author

Correspondence to Henry Howard-Jenkins .

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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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Howard-Jenkins, H., Li, Y., Prisacariu, V.A. (2020). GroSS: Group-Size Series Decomposition for Grouped Architecture Search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12371. Springer, Cham. https://doi.org/10.1007/978-3-030-58574-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-58574-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58573-0

  • Online ISBN: 978-3-030-58574-7

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