Back to articles
Volume: 32 | Article ID: art00023
  DOI :  10.2352/ISSN.2470-1173.2020.10.IPAS-312  Published OnlineJanuary 2020

Generative adversarial networks (GANs) have been significantly investigated in the past few years due to its outstanding data generation capacity. The extensive use of the GANs techniques is dominant in the field of computer vision, for example, plausible image generation, image to image translation, facial attribute manipulation, improving image resolution, and image to text translation. In spite of the significant success achieved in these domains, applying GANs to various other problems still presents important challenges. Several reviews and surveys for GANs are available in the literature. However, none of them present short but focused review about the most significant aspects of GANs. In this paper, we address these aspects. We analyze the basic theory of GANs and the differences among various generative models. Then, we discuss the recent spectrum of applications covered by the GANs. We also provide an insight into the challenges and future directions.

Subject Areas :
Views 22
Downloads 2
 articleview.views 22
 articleview.downloads 2
  Cite this article 

Habib Ullah, Sultan Daud Khan, Mohib Ullah, Maqsood Mahmud, Faouzi Alaya Cheikh, "GENERATIVE ADVERSARIAL NETWORKS: A SHORT REVIEWin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVIII,  2020,  pp 312-1 - 312-7,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA