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  • ARE GENERATIVE ADVERSARIAL NETWORKS CAPABLE OF GENERATING NOVEL AND DIVERSE DESIGN CONCEPTS? AN EXPERIMENTAL ANALYSIS OF PERFORMANCE
Title: ARE GENERATIVE ADVERSARIAL NETWORKS CAPABLE OF GENERATING NOVEL AND DIVERSE DESIGN CONCEPTS? AN EXPERIMENTAL ANALYSIS OF PERFORMANCE
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.  more » « less
Award ID(s):
2050052
PAR ID:
10590700
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Design Society
Date Published:
Journal Name:
Proceedings of the Design Society
Volume:
3
ISSN:
2732-527X
Page Range / eLocation ID:
633 to 644
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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