Generative adversarial network: An overview of theory and applications

TLDR

Image segmentation, a key computer‑vision task, has grown with deep learning and GANs, enabling diverse applications such as 3D generation, medicine, and traffic control, especially since 2016. The study systematically reviews recent GAN models and applications while outlining challenges and future research directions. The authors conducted a systematic review using Embase, Scopus, Web of Science, and PubMed to locate relevant GAN studies. From 2,084 identified papers, 52 met inclusion criteria for the final review.

Abstract

In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision undertakings as it needs low-level spatial data. Especially, Deep Learning has impacted the field of segmentation incredibly and gave us today different successful models. The deep learning associated Generated Adversarial Networks (GAN) has presenting remarkable outcomes on image segmentation. In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area. Search outcomes have identified 2084 documents, after two-phase screening 52 potential records are included for final review. The following applications of GAN have been emerged: 3D object generation, medicine, pandemics, image processing, face detection, texture transfer, and traffic controlling. Before 2016, research in this field was limited and thereafter its practical usage came into existence worldwide. The present study also envisions the challenges associated with GAN and paves the path for future research in this realm.