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ARE GENERATIVE ADVERSARIAL NETWORKS CAPABLE OF GENERATING NOVEL AND DIVERSE DESIGN CONCEPTS? AN EXPERIMENTAL ANALYSIS OF PERFORMANCE

Published online by Cambridge University Press:  19 June 2023

Parisa Ghasemi ,
Chenxi Yuan ,
Tucker Marion  and
Mohsen Moghaddam
Parisa Ghasemi
Affiliation:
Northeastern University
Chenxi Yuan
Affiliation:
Northeastern University
Tucker Marion
Affiliation:
Northeastern University
Mohsen Moghaddam*
Affiliation:
Northeastern University
*
Moghaddam, Mohsen, Northeastern University, United States of America, mohsen@northeastern.ed

Abstract

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Generative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design.

Keywords

Machine learningArtificial intelligenceUser centred designGenerative designGenerative adversarial networks
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 633 - 644
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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