Generative Adversarial Networks in Business and Social Science
Source abstract
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 they have 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 of and advancements 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, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Generative Adversarial Networks and Image Synthesis · Computational and Text Analysis Methods · Digital Media Forensic Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department Applied Economic I, Faculty of Sciences of Economics and Business, University of Seville, Avda. Ramón y Cajal, 1, E-41018 Sevilla, Spain
Funding
Spanish Ministry of Science, Innovation and Universities (AEI/FEDER, UE)
References (100+)
Show source-provided references
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