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Global balanced iterative pruning for efficient convolutional neural networks

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Abstract

With the increase of structure complexity, convolutional neural networks (CNNs) take a fair amount of computation cost. Meanwhile, existing research reveals the salient parameter redundancy in CNNs. The current pruning methods can compress CNNs with little performance drop, but when the pruning ratio increases, the accuracy loss is more serious and the compressing rates of parameters and floating-point operations (FLOPs) are unbalanced. Moreover, the existing iterative pruning methods are difficult to accurately identify and delete unimportant parameters due to the accuracy drop during pruning. We propose a novel global balanced iterative pruning method (GBIP) for CNNs. Firstly, a global equilibrium pruning strategy based on feature distribution is proposed. Then the intermediate and output features of original network are applied to guide the fine-tuning of pruned network. Moreover, we design a shallow fully-connected network to allow the output of two networks to play an adversarial game, thereby it can quickly recover the pruned accuracy among iterative pruning intervals. We conduct extensive experiments on the image classification tasks CIFAR-10, CIFAR-100, and ILSVRC-2012 to verify our pruning method can achieve efficient compression for CNNs even without accuracy loss. On the ILSVRC-2012, when removing 36.78% parameters and 45.55% FLOPs of ResNet-18, the Top-1 accuracy drop are only 0.66%. Our method is superior to some state-of-the-art pruning schemes in terms of compressing rate and accuracy. Moreover, we further demonstrate that GBIP has good generalization on the object detection task PASCAL VOC.

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Acknowledgements

This work was supported in part by the Anhui Provincial Key Research and Development Program under Grant 202004a05020040, in part by the National Key Research and Development Program under Grant 2018YFC0604404, in part by Intelligent Network and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of HFUT under Grant IMIWL2019003, and in part by Fundamental Research Funds for the Central Universities under Grant PA2021GDGP0061.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Jingfei Chang, Yang Lu, Ping Xue, Yiqun Xu & Zhen Wei

  2. Anhui Mine IOT and Security Monitoring Technology Key Laboratory, Hefei, 230088, China

    Yang Lu & Zhen Wei

  3. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Hefei University of Technology, Ministry of Education, Hefei, 230009, China

    Yang Lu & Zhen Wei

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  1. Jingfei Chang
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Chang, J., Lu, Y., Xue, P. et al. Global balanced iterative pruning for efficient convolutional neural networks. Neural Comput & Applic 34, 21119–21138 (2022). https://doi.org/10.1007/s00521-022-07594-7

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