A Compact Parallel Pruning Scheme for Deep Learning Model and Its Mobile Instrument Deployment

Meng Li, Ming Zhao, Tie Luo, Yimin Yang and Sheng-Lung Peng
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Meng Li: School of Computer Science, Yangtze University, Jingzhou 434025, China
Ming Zhao: School of Computer Science, Yangtze University, Jingzhou 434025, China
Tie Luo: School of Computer Science, Yangtze University, Jingzhou 434025, China
Yimin Yang: School of Computer Science, Yangtze University, Jingzhou 434025, China
Sheng-Lung Peng: Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei 10051, Taiwan

Mathematics, 2022, vol. 10, issue 12, 1-17

Abstract: In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model. Directly pruning the filter will largely cause the loss of key information and affect the accuracy of model classification. To solve these problems, a parallel pruning algorithm combined with image enhancement is proposed. Firstly, in order to improve the generalization ability of the model, a data enhancement method of random erasure is introduced. Secondly, according to the trained batch normalization layer scaling factor, the channels with small contribution are cut off, the model is initially thinned, and then the filters are pruned. By calculating the geometric median of the filters, redundant filters similar to them are found and pruned, and their similarity is measured by calculating the distance between filters. Pruning was done using VGG19 and DenseNet40 on cifar10 and cifar100 data sets. The experimental results show that this algorithm can improve the accuracy of the model, and at the same time, it can compress the calculation and parameters of the model to a certain extent. Finally, this method is applied in practice, and combined with transfer learning, traffic objects are classified and detected on the mobile phone.

Keywords: image preprocessing; deep convolution neural network; pruning; model compression; channel; filter; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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