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Exploring Fine-Grained Sparsity in Convolutional Neural Networks for Efficient Inference.

Longguang Wang, Yulan Guo, Xiaoyu Dong

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    This study introduces a novel sparse mask mechanism for convolutional neural networks, significantly enhancing inference efficiency by reducing redundant computations. This method improves performance on resource-limited devices for various computer vision tasks.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Artificial Intelligence

    Background:

    • Neural networks often contain substantial redundant computations, limiting inference efficiency.
    • Deployment on resource-constrained devices is challenging due to high computational demands.

    Purpose of the Study:

    • To propose a generic sparse mask mechanism for improving convolutional neural network inference efficiency.
    • To dynamically localize and skip redundant computations at a fine-grained level.

    Main Methods:

    • Learned sparse masks in both data and channel dimensions.
    • Developed specialized models: SMPointSeg, SMSR, and SMStereo.
    • Evaluated compatibility with diverse model components and architectures.

    Main Results:

    • Sparse masks effectively localize redundant computations.
    • Significant reduction in computational cost leading to practical speedups.
    • Demonstrated compatibility across different network architectures.

    Conclusions:

    • The proposed sparse mask mechanism enhances inference efficiency and accuracy.
    • Achieved state-of-the-art performance on benchmark datasets for segmentation, super-resolution, and stereo matching.
    • The approach is suitable for various computer vision tasks on resource-limited devices.