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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks.

Yao Lu, Guangming Lu, Rui Lin

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    |September 11, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a novel repeated group convolutional (RGC) kernel and sparse RGC (SRGC) kernel for mobile networks. SRGC-Nets significantly reduce model size and computational complexity while improving performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Group convolution is prevalent in mobile networks for channel redundancy reduction.
    • Existing methods still have redundancy in the group extent.
    • Efficient convolutional kernels are crucial for mobile network performance.

    Purpose of the Study:

    • To introduce a novel repeated group convolutional (RGC) kernel to further reduce redundancy.
    • To propose a sparse RGC (SRGC) kernel and SRGC neural networks (SRGC-Nets).
    • To enhance model efficiency and performance in mobile networks.

    Main Methods:

    • Proposed a repeated group convolutional (RGC) kernel with M primary and N tiny groups.
    • Introduced a sparse RGC (SRGC) kernel by combining RGC and pointwise group convolutional (PGC) kernels.
    • Developed SRGC neural networks (SRGC-Nets) based on the SRGC kernel.

    Main Results:

    • SRGC significantly reduces parameters and computational complexity.
    • SRGC-Nets preserve spatial and channel-difference features effectively.
    • Comparative experiments show SRGC-Nets outperform traditional and state-of-the-art mobile networks.

    Conclusions:

    • SRGC-Nets offer a superior trade-off between model size, computational complexity, and performance.
    • The proposed SRGC kernel is effective in reducing redundancy and improving feature richness.
    • SRGC-Nets demonstrate potential for efficient deployment in mobile applications.