TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Generative Adversarial Networks: A Literature Review |
| 영문제목(English Title) |
Generative Adversarial Networks: A Literature Review |
| 저자(Author) |
Jieren Cheng
Yue Yang
Xiangyan Tang
Naixue Xiong
Yuan Zhang
Feifei Lei
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| 원문수록처(Citation) |
VOL 14 NO. 12 PP. 4625 ~ 4647 (2020. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of “generative” and “adversarial”, researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.
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| 키워드(Keyword) |
Generative Adversarial Networks
Artificial Intelligence
Generative Model
Deep Learning
Security
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| 파일첨부 |
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