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A comprehensive survey of convolutions in deep learning: applications, challenges, and future trends

Younesi, A ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1109/ACCESS.2024.3376441
  3. Publisher: 2024
  4. Abstract:
  5. 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. © 2013 IEEE
  6. Keywords:
  7. 6D vision ; Attention ; CNN ; Deep learning ; Depthwise,NAS,NAT ; Dilated convolution ; DNN ; GAN ; Large language model ; Machine learning ; Transformer ; Vision language model ; Vision transformers ; Computer vision ; Convolution ; Deep neural networks ; Generative adversarial networks ; Job analysis ; Network architecture ; Object detection ; Classification algorithm ; Convolutional neural network ; Depthwise ; Dilated convolution ; Images segmentations ; Large language model ; LLM ; NAS ; NAT ; Objects detection ; Performances evaluation ; Task analysis ; Object recognition
  8. Source: IEEE Access ; Volume 12 , 2024 , Pages 41180-41218 ; 21693536 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/10466766