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

TL;DR

This survey comprehensively reviews convolutional neural networks, covering their history, models, applications across various dimensions, and future prospects, aiming to provide a broad and insightful overview of the field.

Contribution

It offers a novel, broad perspective on CNNs, including recent ideas, multi-dimensional convolutions, and practical guidelines, filling gaps left by previous reviews.

Findings

Analysis of CNN model performance and key design principles

Guidelines for function selection in CNN architectures

Discussion of open issues and future research directions

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.