Dynamic Network Structured Pruning via Feature Coefficients of Layer Fusion

被引：0

作者：

Lu H. ^{[1
]}

Yuan X. ^{[1
]}

机构：

[1] School of Automation, Nanjing University of Information Science and Technology, Nanjing

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Dynamic Parameters; Layer Fusion Feature Coefficient; Model Complexity; Structured Pruning;

D O I：

10.16451/j.cnki.issn1003-6059.201911010

中图分类号：

学科分类号：

摘要：

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.

引用

页码：1051 / 1059

页数：8

共 21 条

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