Application of deep learning in digital product design: product innovation through generative adversarial networks

Wen Sun

doi:10.1117/12.3026808

15 March 2024 Application of deep learning in digital product design: product innovation through generative adversarial networks

Wen Sun

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751V (2024) https://doi.org/10.1117/12.3026808
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

This paper explores the potential of Generative Adversarial Networks (GANs) in icon generation for digital product design. Icons are essential visual elements in user interfaces, facilitating communication and enhancing user experience. Traditional icon design methods often require considerable manual effort and time. By harnessing the power of GANs, this study investigates how automated icon generation can streamline the design process and improve the quality and diversity of icons. This study concludes the building of the model for generating icons by integrating the generative adversarial network model with the icon design procedure itself. In order to solve the issue of creating icons automatically, this study conducts the following studies. 1) A Multi-Feature Icon Generation Network (MFIGN) is suggested, with its foundation in the conditional classification generative adversarial network. To guarantee that the icon produced by the model satisfies the supplied conditional feature, a multi-feature identification module has been introduced to the discriminator. 2) The icon generation model suggested in this paper outperforms existing methods on the icon dataset when it comes to the design of a wide range of icon feature expressions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wen Sun "Application of deep learning in digital product design: product innovation through generative adversarial networks", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751V (15 March 2024); https://doi.org/10.1117/12.3026808

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