Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions
Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided...
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| Published in: | Computation Vol. 11; no. 3; p. 52 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Basel
MDPI AG
01.03.2023
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| ISSN: | 2079-3197, 2079-3197 |
| Online Access: | Get full text |
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| Abstract | Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues. |
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| AbstractList | Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues. |
| Audience | Academic |
| Author | Taye, Mohammad Mustafa |
| Author_xml | – sequence: 1 givenname: Mohammad Mustafa orcidid: 0009-0000-9248-7423 surname: Taye fullname: Taye, Mohammad Mustafa |
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