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

Parisa Ghasemi, Chenxi Yuan, Tucker Marion, Mohsen Moghaddam

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)633-643
Number of pages11
JournalProceedings of the Design Society
Volume3
DOIs
StatePublished - 2023
Externally publishedYes
Event24th International Conference on Engineering Design, ICED 2023 - Bordeaux, France
Duration: Jul 24 2023Jul 28 2023

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software
  • Modeling and Simulation

Keywords

  • Artificial intelligence
  • Generative adversarial networks
  • Generative design
  • Machine learning
  • User centred design

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