A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing
Ankan Dash, Junyi Ye, Guiling Wang

TL;DR
This paper provides a comprehensive review of Generative Adversarial Networks (GANs), their variants, evaluation metrics, and diverse applications across disciplines from medical imaging to remote sensing and arts.
Contribution
It offers the most extensive survey of GAN applications across various fields, explaining underlying theory, variants, and practical uses in 12 domains.
Findings
GANs enable high-quality image and video generation.
GANs are applied in scientific data processing and synthesis.
GANs have broad interdisciplinary applications, from biology to arts.
Abstract
We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection · Advanced Image Processing Techniques
