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WeightNet: Revisiting the Design Space of Weight Networks

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12360))

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Abstract

We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective SENet and CondConv into the same framework on weight space. The method, called WeightNet, generalizes the two methods by simply adding one more grouped fully-connected layer to the attention activation layer. We use the WeightNet, composed entirely of (grouped) fully-connected layers, to directly output the convolutional weight. WeightNet is easy and memory-conserving to train, on the kernel space instead of the feature space. Because of the flexibility, our method outperforms existing approaches on both ImageNet and COCO detection tasks, achieving better Accuracy-FLOPs and Accuracy-Parameter trade-offs. The framework on the flexible weight space has the potential to further improve the performance. Code is available at https://github.com/megvii-model/WeightNet.

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Acknowledgements

This work is supported by The National Key Research and Development Program of China (No. 2017YFA0700800) and Beijing Academy of Artificial Intelligence (BAAI).

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Sai Kung, Hong Kong

    Ningning Ma

  2. MEGVII Technology, Beijing, China

    Xiangyu Zhang, Jiawei Huang & Jian Sun

Authors
  1. Ningning Ma
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  2. Xiangyu Zhang
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  3. Jiawei Huang
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  4. Jian Sun
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Corresponding author

Correspondence to Xiangyu Zhang .

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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Ma, N., Zhang, X., Huang, J., Sun, J. (2020). WeightNet: Revisiting the Design Space of Weight Networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_46

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  • DOI: https://doi.org/10.1007/978-3-030-58555-6_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58554-9

  • Online ISBN: 978-3-030-58555-6

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