Survey of Convolutional Neural Network

DOI

10.2991/ncce-18.2018.16How to use a DOI?

Keywords

Convolutional Neural Network; deep learning; image processing; computer vision.

Abstract

In recent years, the breakthrough of deep learning in the field of artificial intelligence algorithms has triggered an academic upsurge which attracted more and more researchers. As a multi-layer perceptron, the key to its success lies in the local link and weight-sharing method. On the one hand, it reduces the quantity of weights and makes the network easier to optimize. On the other hand, it reduces the risk of over-fitting. A weight-sharing network’s structure of the convolutional neural network makes it more similar to a biological neural network, which reduces the complexity of the network model and quantity of weights. In the processing of image problems, especially recognizing displacement, scaling, and other forms of distortion invariant applications, it has better robustness and operation efficiency. First of all, this paper reviews the development history of convolutional neural network. Secondly, it introduces the basic structure of convolutional neural network, and elaborates its differences from ordinary artificial neural networks in terms of operating principles. It also analyzes the details of convolutional neural network’s structural framework which includes convolutional layers, subsampling layers, and fully connected layers. Finally, the advantages of convolutional neural network in image processing, speech analysis, and other fields are given at last.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xv Zhang
AU  - Chenxi Xv
AU  - Ming Shen
AU  - Xin He
AU  - Wei Du
PY  - 2018/05
DA  - 2018/05
TI  - Survey of Convolutional Neural Network
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 93
EP  - 97
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.16
DO  - 10.2991/ncce-18.2018.16
ID  - Zhang2018/05
ER  -