卷积神经网络研究综述

doi:10.11772/j.issn.1001-9081.2016.09.2508

摘要/Abstract

摘要： 近年来，卷积神经网络在图像分类、目标检测、图像语义分割等领域取得了一系列突破性的研究成果，其强大的特征学习与分类能力引起了广泛的关注，具有重要的分析与研究价值。首先回顾了卷积神经网络的发展历史，介绍了卷积神经网络的基本结构和运行原理，重点针对网络过拟合、网络结构、迁移学习、原理分析四个方面对卷积神经网络在近期的研究进行了归纳与分析，总结并讨论了基于卷积神经网络的相关应用领域取得的最新研究成果，最后指出了卷积神经网络目前存在的不足以及未来的发展方向。

关键词: 卷积神经网络, 深度学习, 特征表达, 神经网络, 迁移学习

Abstract: In recent years, Convolutional Neural Network (CNN) has made a series of breakthrough research results in the fields of image classification, object detection, semantic segmentation and so on. The powerful ability of CNN for feature learning and classification attracts wide attention, it is of great value to review the works in this research field. A brief history and basic framework of CNN were introduced. Recent researches on CNN were thoroughly summarized and analyzed in four aspects: over-fitting problem, network structure, transfer learning and theoretic analysis. State-of-the-art CNN based methods for various applications were concluded and discussed. At last, some shortcomings of the current research on CNN were pointed out and some new insights for the future research of CNN were presented.

Key words: Convolutional Neural Network (CNN), deep learning, feature representation, neural network, transfer learning

中图分类号:

TP181

李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9): 2508-2515.

LI Yandong, HAO Zongbo, LEI Hang. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2515.

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