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Towards efficient filter pruning via topology

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Abstract

With the development of deep neural networks, compressing and accelerating deep neural networks without performance deterioration has become a research hotspot. Among all kinds of network compression methods, network pruning is one of the most effective and popular methods. Inspired by several property-based pruning methods and geometric topology, we focus the research of the pruning method on the extraction of feature map information. We predefine a metric, called TopologyHole, used to describe the feature map and associate it with the importance of the corresponding filter. In the exploration experiments, we find out that the average TopologyHole of the feature map for the same filter is relatively stable, regardless of the number of image batches the CNNs receive. This phenomenon proves TopologyHole is a data-independent metric and valid as a criterion for filter pruning. Through a large number of experiments, we have demonstrated that priorly pruning the filters with high-TopologyHole feature maps achieves competitive performance compared to the state-of-the-art. Notably, on ImageNet, TopologyHole reduces 45.0\(\%\) FLOPs by removing 40.9\(\%\) parameters on ResNet-50 with 75.71\(\%\), only a loss of 0.44\(\%\) in top-1 accuracy.

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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Xiaozhou Xu, Jun Chen, Hongye Su & Lei Xie

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  1. Xiaozhou Xu
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  2. Jun Chen
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  3. Hongye Su
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  4. Lei Xie
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Correspondence to Lei Xie.

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Xu, X., Chen, J., Su, H. et al. Towards efficient filter pruning via topology. J Real-Time Image Proc 19, 639–649 (2022). https://doi.org/10.1007/s11554-022-01209-z

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