基于层融合特征系数的动态网络结构化剪枝

doi:10.16451/j.cnki.issn1003-6059.201911010

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Abstract Pruning is an effective way to reduce the complexity of the model. In the existing pruning methods, only the influence of the convolutional layer on the feature map is taken into account,and therefore the redundant filter cannot be determined accurately. In this paper, a dynamic network structured pruning method based on layer fusion feature coefficients is proposed. Considering the influence of convolutional layer and Batch Norm layer on the feature map, the importance of the filter is determined by multiple dynamic parameters, and the redundant filter is dynamically searched to obtain the optimal network structure. Experiments on the standard datasets of CIFAR-10 and CIFAR-100 shows that both the residual network and the lightweight network maintain high accuracy while using large pruning rates.

Key words： Model Complexity Layer Fusion Feature Coefficient Structured Pruning Dynamic Parameters

Received: 15 August 2019

ZTFLH:

TP 183

Fund:Supported by National Natural Science Foundation of China(No.61876090,61936005 ), National Major Project of China for New Generation of AI(No. 2018AAA0100401)

Corresponding Authors: YUAN Xiaotong, Ph.D., professor. His research interests include machine learning and pattern recognition.

About author:: LU Haiwei, master student. His research interests include neural network model compression.

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Cite this article:

LU Haiwei,YUAN Xiaotong. Dynamic Network Structured Pruning via Feature Coefficients of Layer Fusion[J]. , 2019, 32(11): 1051-1056.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201911010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I11/1051

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