Method for Reducing Neural-Network Models of Computer Vision

被引：1

作者：

Kroshchanka, A. A. ^{[1
]}

Golovko, V. A. ^{[1
,2
]}

Chodyka, M. ^{[2
]}

机构：

[1] Brest State Tech Univ Educ Estab, Ul Moskovskaya 267, Brest 224017, BELARUS

[2] Akad Bialska Nauk Stosowanych Jana Pawla II, Sidorska Ul 95-97,P 271R, PL-21500 Biala Podlaska, Poland

来源：

PATTERN RECOGNITION AND IMAGE ANALYSIS | 2022年 / 32卷 / 02期

关键词：

deep neural networks; reduction of neural network parameters; pretraining of deep neural networks; computer vision; ALGORITHM;

D O I：

10.1134/S1054661822020146

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This article proposes an approach to reducing fully connected neural networks using classical and modified pretraining of deep neural networks. The authors have demonstrated that this approach can significantly reduce the number of parameters of the trained neural network with little or no reduction in the generalizing ability. The capabilities of the proposed method are demonstrated on classical computer vision datasets.

引用

页码：294 / 300

页数：7

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