A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

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.

arXiv comment21 pages, 33 figures, journal

References in corpus (26)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• YOLOv3: An Incremental Improvement
• ADADELTA: An Adaptive Learning Rate Method
• Neural Architecture Search with Reinforcement Learning
• In Defense of the Triplet Loss for Person Re-Identification
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Deep Learning Face Representation by Joint Identification-Verification
• On the Convergence of Adam and Beyond
• DeepID3: Face Recognition with Very Deep Neural Networks
• DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
• Speeding up Convolutional Neural Networks with Low Rank Expansions
• Action Recognition using Visual Attention
• Rigid-Motion Scattering for Texture Classification
• Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
• A feature agnostic approach for glaucoma detection in OCT volumes
• Fake News Detection on Social Media using Geometric Deep Learning
• Why do deep convolutional networks generalize so poorly to small image transformations?
• CornerNet-Lite: Efficient Keypoint Based Object Detection
• Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
• Towards the Limit of Network Quantization
• Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model
• Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
• Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
• Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification

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