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

TL;DR

This survey comprehensively reviews various CNN architectures, analyzing their structures, applications, challenges, and future trends to guide future research and development in deep learning.

Contribution

It provides a detailed comparison of CNN types, their strengths, limitations, and practical applications, along with insights into research platforms and emerging fields.

Findings

Different CNN types have unique structures suited for specific tasks.

Performance analysis reveals strengths and weaknesses of each CNN architecture.

Future trends include novel architectures and applications in emerging fields.

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.