Generative adversarial network: An overview of theory and applications
Open accessAbstract
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.
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GANs have been used for tasks such as image translation, data augmentation, and even generating deepfake videos.
“They have been used for tasks such as image translation, data augmentation, and even generating deepfake videos [20].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [20]
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- Authors
- Aggarwal, A., Mittal, M., & Battineni, G.
- Affiliations
- Università di Camerino (IT)
- Corresponding author
- Battineni, Gopi
- Venue
- International Journal of Information Management Data Insights
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 1
- Issue
- 1
- Pages
- 100004
- ISSN
- 2667-0968 (ISSN-L)
- OpenAlex
- W3128607711
- Cited by
- 783
- References
- 69
- Citations by year
- 2026: 69 · 2025: 218 · 2024: 203 · 2023: 177 · 2022: 85 · 2021: 31
- FWCI
- 44.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Information Management Data Insights (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- INFM-OAR (INFN Catania) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- generative adversarial networks, image segmentation, deep learning, 3D object generation, medical image segmentation, face detection, texture transfer, traffic management, pandemic forecasting, image processing
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