AUTHOR=Hughes Rowan T. , Zhu Liming , Bednarz Tomasz 

TITLE=Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.604234

DOI=10.3389/frai.2021.604234

ISSN=2624-8212

ABSTRACT=The future of work and the workplace is very much in flux. There has been a vast amount written on the topic of Artificial Intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of job losses, etc. In this survey, we will address one area where AI is being added to the toolbox of creative and design practitioners to aid and enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype given a set of constraints. We have seen AI encroaching into this space with the advent generative networks, and generative adversarial networks (GANs) in particular. This area has become one of the most active fields of research in Machine Learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. In this review we look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. From 204 search results, 26 studies (including 2 snowball sampled) are reviewed, highlighting key trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.