Generative adversarial network: An overview of theory and applications

By Alankrita Aggarwal, Mamta Mittal, Gopi Battineni. Int. J. Inf. Manag. Data Insights, 2021

742 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e4807f16e4cb7b9eb7e3d2ab038baeb6ebc6e6b4

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Citations in the corpus

Generative Adversarial Networks for Face Generation: A Survey (2022, background): “The first GANs focused on data augmentation to reduce the overfitting of deep neural networks [2, 138].”
Generative artificial intelligence (2023, background): “…find various applications, for instance, in image generation and manipulation, object detection and segmentation, and natural language processing (Aggarwal et al., 2021; Gui et al., 2023) Variational autoencoder (VAE) V ariational autoencoders employ a neural network to learn encoding compressed…”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, background): “Generally, GAN network deployment is designed for unsupervised learning tasks, but it has also proven to be a better solution for semi-supervised and reinforcement learning as well depending on the task [3].”
RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion (2022, methodology): “Generative models [2, 34] are one of the most promising ways to analyze and synthesize visual data including 2D images and 3D models.”
Deep learning in drug discovery: an integrative review and future challenges (2022, background): “The Discriminator helps to discriminate between real and fake data while the Generator Network creates bogus data (Alankrita et al. 2021).”
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, background): “Generative Adversarial Networks (GANs) have introduced numerous applications across various domains, including image blending [ 68 ], 3D object generation [ 69 ], face aging [ 70 ], medicine [ 71 , 72 ], steganography [ 73 ], image manipulation [ 74 ], text transfer [ 75 ], language and speech synth”