Article
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Generative Adversarial Networks in Business and Social Science
Version 1
: Received: 25 July 2024 / Approved: 25 July 2024 / Online: 26 July 2024 (08:24:57 CEST)
A peer-reviewed article of this Preprint also exists.
Ruiz-Gándara, A.; Gonzalez-Abril, L. Generative Adversarial Networks in Business and Social Science. Appl. Sci. 2024, 14, 7438. Ruiz-Gándara, A.; Gonzalez-Abril, L. Generative Adversarial Networks in Business and Social Science. Appl. Sci. 2024, 14, 7438.
Abstract
Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.
Keywords
GANs; multidisciplinary application; business economics; artificial intelligence; machine learning
Subject
Business, Economics and Management, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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