Author:
Hughes Rowan T.,Zhu Liming,Bednarz Tomasz
Abstract
The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to 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 of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields 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. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations 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.
Funder
Commonwealth Scientific and Industrial Research Organization
Reference102 articles.
1. Applications of generative adversarial networks (gans): an updated review;Alqahtani;Arch. Computat Methods Eng.,2019
2. Social ways: learning multi-modal distributions of pedestrian trajectories with GANs;Amirian,2019
3. Wasserstein GAN;Arjovsky,2017
4. Gan dissection: visualizing and understanding generative adversarial networks;Bau,2019
5. Explanation and justification in machine learning: a survey;Biran,2017
Cited by
46 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献