[论文解读] A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
本论文对卷积神经网络（CNN）从发展史到现状进行综述，涵盖一维、二维及多维卷积CNN，并提供实验洞察与未来方向。
Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years. The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible. Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved. First, this review starts with a brief introduction to the history of CNN. Second, we provide an overview of CNN. Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection. Fifth, the applications of one-dimensional, two-dimensional, and multi-dimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.
研究动机与目标
- 提供对CNN历史与发展的广泛概览。
- 总结经典与先进的CNN模型及其关键创新。
- 提供实验洞察和关于函数选择的实用指南。
- 讨论在一维、二维及多维卷积中的应用。
- 识别尚待解决的问题与未来研究的有前景方向。
提出的方法
- 综合历史背景与CNN概览。
- 评述经典与前沿的CNN架构及其关键创新。
- 分析实验结果以得出结论和关于函数选择的经验规则。
- 讨论贯穿一维、二维及多维卷积的应用。
- 突出尚待解决的问题并提出未来研究方向。
实验结果
研究问题
- RQ1CNN历史与模型设计中的关键里程碑与发展是什么？
- RQ2从CNN实验中可以得出哪些经验性结论和实用的规则？
- RQ3一维、二维及多维卷积在各领域如何应用？
- RQ4CNN研究存在哪些尚待解决的问题和有前景的方向？
主要发现
- 本综述旨在为快速发展中的CNN领域提供新思路与前景。
- 它涵盖一维、二维及多维卷积。
- 它提供CNN历史、模型及使其达到前沿水平的关键要点的概览。
- 实验分析得出关于函数选择的结论与实用经验规则。
- 论文讨论应用并识别尚待解决的问题与未来方向。
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