Convolutional neural networks progress : architectural and optimization methods survey
Other Title(s)
تطور الشبكات العصبية التلافيفية : دراسة استقصائية عن بنية الشبكات العصبية و طرق تحسينها
Joint Authors
Rafat, Muhsin
Fahmi, Husam A. H.
Rashwan, Muhsin
Source
The Egyptian Journal of Language Engineering
Issue
Vol. 8, Issue 2 (30 Sep. 2021), pp.44-68, 25 p.
Publisher
Egyptian Society of Language Engineering
Publication Date
2021-09-30
Country of Publication
Egypt
No. of Pages
25
Main Subjects
Topics
Abstract EN
Since the start of the Convolutional Neural Networks (CNN) paradigm, they were applied in a wide range of computer vision tasks such as image classification, object detection, localization, tracking and action recognition where they were able to show breakthrough performance and generate a new state of the art results.
This paper surveys the progress of CNN from an architectural and optimization perspective.
While many CNN reviews exist in the literature, most of them had focused on providing a survey either from a network architecture prospective or an application one, unlike this one which provides a brief general overview for the key features of CNN, followed by reviewing the progress of the state of the art architectures and finally considers the change in the merit of figure of how the CNN are evaluated to include the optimization methods to provide practical CNN that can be deployed on today's hardware infrastructure without significantly impacting the achieved accuracy.
American Psychological Association (APA)
Rafat, Muhsin& Fahmi, Husam A. H.& Rashwan, Muhsin. 2021. Convolutional neural networks progress : architectural and optimization methods survey. The Egyptian Journal of Language Engineering،Vol. 8, no. 2, pp.44-68.
https://search.emarefa.net/detail/BIM-1307145
Modern Language Association (MLA)
Rafat, Muhsin…[et al.]. Convolutional neural networks progress : architectural and optimization methods survey. The Egyptian Journal of Language Engineering Vol. 8, no. 2 (Sep. 2021), pp.44-68.
https://search.emarefa.net/detail/BIM-1307145
American Medical Association (AMA)
Rafat, Muhsin& Fahmi, Husam A. H.& Rashwan, Muhsin. Convolutional neural networks progress : architectural and optimization methods survey. The Egyptian Journal of Language Engineering. 2021. Vol. 8, no. 2, pp.44-68.
https://search.emarefa.net/detail/BIM-1307145
Data Type
Journal Articles
Language
English
Notes
-
Record ID
BIM-1307145