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Research and Verification of Convolutional Neural Network Lightweight in BCI

文献类型： 外文期刊

作者： Xu, Shipu 1 ; Li, Runlong 3 ; Wang, Yunsheng 2 ; Liu, Yong 2 ; Hu, Wenwen 2 ; Wu, Yingjing 2 ; Zhang, Chenxi 1 ; Liu, Cha 1 ;

作者机构： 1.Tongji Univ, Dept Software Engn, Shanghai 201804, Peoples R China

2.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China

3.Shanghai Inst Technol, Dept Railway Transportat, Shanghai 201418, Peoples R China

4.Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330038, Jiangxi, Peoples R China

期刊名称：COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE （ 影响因子：2.238； 五年影响因子：2.477 ）

ISSN： 1748-670X

年卷期： 2020 年 2020 卷

页码：

收录情况： SCI

摘要： With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.

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