mlsanityData to 7 Oct 2026

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

arXiv 2004.02806 ↗First posted 1 Apr 2020cs.CV cs.LG eess.IVcited by 396

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Abstract (arXiv)

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.

Key facts. Cited by 396 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 40; 2023: 80; 2024: 116; 2025: 106; 2026: 38.

396 papers.

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  11. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
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38 more
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  17. 2023Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022cited by 16
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  24. 2023FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computingcited by 12
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  26. 2025Machine learning for modelling unstructured grid data in computational physics: a reviewcited by 12
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  30. 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Frameworkcited by 11
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