Overview of Capsule Neural Networks

被引：5
|
作者：
Sun, Zengguo [1 ,2 ]
Zhao, Guodong [2 ]
Scherer, Rafal [3 ]
Wei, Wei [4 ]
Wozniak, Marcin [5 ]
机构：
[1] Minist Educ, Key Lab Modern Teaching Technol, Xian, Shaanxi, Peoples R China
[2] Shaanxi Normal Univ, Sch Comp Sci, Xian, Peoples R China
[3] Czestochowa Tech Univ, Dept Intelligent Comp Syst, Czestochowa, Poland
[4] Xian Univ Technol, Sch Comp Sci & Engn, Xian, Peoples R China
[5] Silesian Tech Univ, Fac Appl Math, Gliwice, Poland
来源：
JOURNAL OF INTERNET TECHNOLOGY | 2022年 / 23卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Capsule network; Dynamic routing Mechanism; Convolutional neural network; Deep learning; CLASSIFICATION; CAPSNET;
D O I：
10.53106/160792642022012301004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As a vector transmission network structure, the capsule neural network has been one of the research hotspots in deep learning since it was proposed in 2017. In this paper, the latest research progress of capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network. Secondly, we analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures. Finally, we compile the applications of capsule network in many fields, including computer vision, natural language, and speech processing. Our purpose in writing this article is to provide methods and means that can be used for reference in the research and practical applications of capsule networks.
引用
收藏
页码：33 / 44
页数：12
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