A survey and identification of generative adversarial network technology-based architectural variants and applications in computer vision

Kirtirajsinh Zala (), Deep Thumar (), Hiren Kumar Thakkar (), Urva Maheshwari () and Biswaranjan Acharya ()
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Kirtirajsinh Zala: Marwadi University
Deep Thumar: Marwadi Education Foundations
Hiren Kumar Thakkar: Pandit Deendayal Energy University
Urva Maheshwari: Marwadi Education Foundations
Biswaranjan Acharya: Marwadi University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 9, No 23, 4594-4615

Abstract: Abstract The surging popularity of generative adversarial networks (GANs) has ignited a wave of innovation in the realm of computer vision, a highly explored subfield of deep learning. GANs are revolutionizing the area of machine learning because they use a game-based training technique. This is in contrast to traditional approaches to machine learning, which center on feature learning and picture production. Several subfields of computer vision have seen tremendous progress thanks to the integration of numerous processing approaches, including image processing, dynamic processing, text, audio, and video processing, as well as generalized generative adversarial networks (GANs). Nevertheless, despite the fact that GANs have made great progress, they still offer promise that has not been fully realized and space for additional development. GANs have a wide range of applications within computer vision, including data augmentation, displacement recording, dynamic modeling, and image processing. This article digs into recent advances made by GAN researchers working in the realm of AI-based security and defense and discusses their accomplishments. In particular, we investigate how well image optimization, image processing, and image stabilization are incorporated into GAN-driven picture training. We want to achieve our goal of providing a complete overview of the present status of GAN research by carefully evaluating research articles that have been subjected to peer review.

Keywords: Generative adversarial networks; Computer vision; Loss-variants; Machine learning; Intelligent computing; Bio informatics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02478-6

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