A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
2020/04/01 by Zewen Li, Li, Zewen, Wenjie Yang +5 · 116 citations
Computer Science · Engineering · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Machine Learning and ELM #acm:68T07 #acm:68T10 #acm:68T45 #cs.CV #cs.LG #eess.IV #msc:68T07 #msc:68T10 #msc:68T45
paper · pdf · doi:10.48550/arxiv.2004.02806
21 pages, 33 figures, journal
arxiv created 2020/04/01 · arxiv updated 2020/04/07
Abstract
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.
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