A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

Abolfazl Younesi, Mohsen Ansari, Mohammadamin Fazli, Alireza Ejlali, Muhammad Shafique, Jorg Henkel

Research output: Contribution to journalArticlepeer-review

Abstract

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

Original languageEnglish (US)
Pages (from-to)41180-41218
Number of pages39
JournalIEEE Access
Volume12
DOIs
StatePublished - 2024

Keywords

  • 6D vision
  • attention
  • CNN
  • computer vision
  • Deep learning
  • depthwise,NAS,NAT
  • dilated convolution
  • DNN
  • GAN
  • large language model
  • LLM
  • machine learning
  • object detection
  • transformer
  • vision language model
  • vision transformers

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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