卷积网络深度学习算法与实例

陈旭; 张军; 陈文伟; 李硕豪

doi:10.12052/gdutxb.170093

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卷积网络深度学习算法与实例

综合研究 | 更新时间：2026-05-27

- 卷积网络深度学习算法与实例
- Convolutional Neural Network Algorithm and Case
- 广东工业大学学报 2017年34卷第6期页码：20-26
- 作者机构：
  
  国防科技大学信息系统与管理学院,湖南,长沙,410073
- 作者简介：
  
  陈旭(1992–)，男，博士研究生，主要研究方向为深度学习和自然场景文字检测
  陈文伟(1940–)男，教授，博士生导师，主要方向为决策支持系统、数据仓库和数据挖掘等. E-mail：chenww93@126.com
- 基金信息：
  
  国家自然科学基金资助项目(6117019)
- DOI：10.12052/gdutxb.170093
  中图分类号： TP391
- 网络首发：2017-11-09，
  
  纸质出版：2017
- 稿件说明：
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陈旭, 张军, 陈文伟, 李硕豪. 卷积网络深度学习算法与实例[J]. 广东工业大学学报, 2017, 34(6): 20-26.

Chen Xu, Zhang Jun, Chen Wen-wei, et al. Convolutional Neural Network Algorithm and Case[J]. 2017, 34(6): 20-26.
陈旭, 张军, 陈文伟, 李硕豪. 卷积网络深度学习算法与实例[J]. 广东工业大学学报, 2017, 34(6): 20-26. DOI： 10.12052/gdutxb.170093.

Chen Xu, Zhang Jun, Chen Wen-wei, et al. Convolutional Neural Network Algorithm and Case[J]. 2017, 34(6): 20-26. DOI： 10.12052/gdutxb.170093.

摘要

卷积神经网络(CNN)和反向传播神经网络结合组成的深度学习模型

是目前应用比较广泛的深度学习模型

但是具体算法有多种. 本文针对以CNN为主体的深度学习算法的实现技术

包括卷积核设计、池化的作用、激活函数的选取和训练过程等问题进行详细讨论

并结合实例进行说明

便于对卷积网络深度学习算法的掌握

使其能得到更广泛的应用. 最后总结并展望卷积神经网络未来的研究方向.

Abstract

Convolutional neural network (CNN) is a deep learning model with strong expression and classification ability

and is currently widely used

but there are a variety of specific algorithms. In this paper

the realization of deep learning algorithm based on convolutional neural network CNN

including the function of convolution kernel

the role of pooling

the selection of activation function and the training process are discussed. And one example is explained

which facilitates the mastery of CNN. Finally

the future research direction of convolution neural network is summarized and forecasted.

关键词

Keywords

references

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.[S.l.]: [s.n.], 2012: 1097-1105.

ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//[s.n.]. European conference on computer vision.[S.l.]: Springer, Cham, 2014: 818-833.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv, 2014: 1409. 1556.

SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l.]: [s.n.], 2015: 1-9.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l.: s.n.], 2016: 770-778.

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l.]: [s.n.], 2014: 580-587.

GIRSHICK R. Fast r-cnn[C]//IEEE.Proceedings of the IEEE international conference on computer vision.[S.l.: s.n.], 2015: 1440-1448.

REN S, HE K, GIRSHICK R, et al . Faster R-CNN: Towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

TIAN Z, HUANG W, HE T, et al. Detecting text in natural image with connectionist text proposal network[C]//European Conference on Computer Vision.[S.l.]: Springer International Publishing, 2016: 56-72.

ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks[J]. arXiv preprint arXiv, 2013: 1301. 3557.

CLEVERT D, UNTERTHINER T, HOCHREITER S, et al. Fast and accurate deep network learning by exponential linear units (elus)[J]. arXiv preprint arXiv, 2015: 1511. 07289.

SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al . Dropout: a simple way to prevent neural networks from overfitting [J ] . Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

SRIVASTAVA R K,GREFF K, SCHMIDHUBER J, et al. Training very deep networks[C]//Advances in neural information processing systems.[S.l.]: [s.n.], 2015: 2377-2385.

GLOROT X, BENGIO Y. Understanding the difficulty of training deep feed forward neural networks [J]. Journal of Machine Learning Research, 2010, 9: 249-256.

HE K, ZHANG X, REN S, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]//IEEE. Proceedings of the IEEE international conference on computer vision.[S.l.]: [s.n.] 2015: 1026-1034.

HE K, ZHANG X, REN S, et al . Spatial Pyramid Pooling in deep convolutional networks for visual recognition [J ] . IEEE Transactions on Pattern Analysis Machine Intelligence, 2014, 37(9): 1904-1916.

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