Research Issues on Generative Adversarial Networks and Applications

Generative Adversarial Networks introduces a challenging problem in the field of image generation and computer vision. Generating especially 3D face images is becoming very significant and difficult. This is openings in many interesting applications including Virtual Reality, Augmented Reality, Comp...

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Published in2020 IEEE International Conference on Big Data and Smart Computing (BigComp) pp. 487 - 488
Main Authors Mukhiddin, Toshpulatov, Lee, WooKey, Lee, Suan, Rashid, Tojiboev
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2020
Subjects
3D-face-generation
Computational modeling
deep-learning
deep-neural-network
Face
Gallium nitride
Generative adversarial networks
generator
Neural networks
Solid modeling
Three-dimensional displays
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Summary:Generative Adversarial Networks introduces a challenging problem in the field of image generation and computer vision. Generating especially 3D face images is becoming very significant and difficult. This is openings in many interesting applications including Virtual Reality, Augmented Reality, Computer games, teleconferencing, Virtual try-on, Special effect, and so on. This paper contains three sections. The first section describes the Generative Adversarial Networks. The second section describes applications and methods and finally, the last section describes the future research directions of Generative Adversarial Networks.
ISSN:2375-9356
DOI:10.1109/BigComp48618.2020.00-19