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Generative Adversarial Network Technologies and Applications in Computer Vision
Joint Authors
Jin, Lianchao
Tan, Fuxiao
Jiang, Shengming
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Computer vision is one of the hottest research fields in deep learning.
The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision.
The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation.
GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields.
However, there are still some problems with GANs, such as model collapse and uncontrollable training.
This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models.
The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration.
The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced.
The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.
American Psychological Association (APA)
Jin, Lianchao& Tan, Fuxiao& Jiang, Shengming. 2020. Generative Adversarial Network Technologies and Applications in Computer Vision. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138708
Modern Language Association (MLA)
Jin, Lianchao…[et al.]. Generative Adversarial Network Technologies and Applications in Computer Vision. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1138708
American Medical Association (AMA)
Jin, Lianchao& Tan, Fuxiao& Jiang, Shengming. Generative Adversarial Network Technologies and Applications in Computer Vision. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138708
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138708