Summary of A Comprehensive Survey Of Convolutions in Deep Learning: Applications, Challenges, and Future Trends, by Abolfazl Younesi et al.
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
by Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli, Alireza Ejlali, Muhammad Shafique, Jörg Henkel
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper surveys the landscape of Convolutional Neural Networks (CNNs), a type of Deep Learning (DL) architecture used for computer vision tasks. The paper explores various CNN types, including 1D, 2D, and 3D networks, dilated, grouped, attention, depthwise convolutions, and NAS, each with its unique structure and characteristics. By comparing and analyzing these different architectures, researchers can gain a deeper understanding of their strengths and weaknesses, ultimately informing the development of new and improved CNNs. The paper also delves into the platforms and frameworks used by researchers and explores research fields like 6D vision, generative models, and meta-learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at different types of computer vision tools called Convolutional Neural Networks (CNNs). Each type has its own special features that make it good for specific tasks. The paper compares these different CNNs to see how they work well or not so well, and what kind of problems they can solve. It also talks about the platforms and frameworks used by researchers to develop new ideas. Overall, this study helps us understand the many types of CNNs and how we can use them to improve our computer vision abilities. |
Keywords
* Artificial intelligence * Attention * Cnn * Deep learning * Meta learning
