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Design, Learning, and Innovation. 5th EAI International Conference, DLI 2020, Virtual Event, December 10-11, 2020, Proceedings

Research Article

Towards the Development of AI Based Generative Design Tools and Applications

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  • @INPROCEEDINGS{10.1007/978-3-030-78448-5_5,
        author={Juan Carlos Chac\^{o}n and Hisa Mart\^{\i}nez Nimi and Bastian Kloss and Ono Kenta},
        title={Towards the Development of AI Based Generative Design Tools and Applications},
        proceedings={Design, Learning, and Innovation. 5th EAI International Conference, DLI 2020, Virtual Event, December 10-11, 2020, Proceedings},
        proceedings_a={DLI},
        year={2021},
        month={6},
        keywords={Generative adversarial networks Generative design Design tools},
        doi={10.1007/978-3-030-78448-5_5}
    }
    
  • Juan Carlos Chacón
    Hisa Martínez Nimi
    Bastian Kloss
    Ono Kenta
    Year: 2021
    Towards the Development of AI Based Generative Design Tools and Applications
    DLI
    Springer
    DOI: 10.1007/978-3-030-78448-5_5
Juan Carlos Chacón1,*, Hisa Martínez Nimi1, Bastian Kloss1, Ono Kenta1
  • 1: Graduate School of Engineering
*Contact email: juancarlos@chiba-u.jp

Abstract

In recent years, several projects that take advantage of Artificial Intelligence as a design tool have arisen. However, most designers lack the technical knowledge necessary to profit from Artificial Intelligence in their design process fully. Through the development of GANSta, a tool with a graphical user interface that facilities the design and training of Generative Adversarial Networks. And the use and application of such a tool in different stages of the design process. By engaging in both iconographic branding element design and typographic font design projects. Participants of the Gesign lab initiative of Chiba University's System Planning Laboratory, explore the current and future opportunities that Generative Adversarial Networks present for their particular design process. Proving that previous knowledge in programming or machine learning is not necessary for designers to take advantage of the benefits that this technology presents from a generative design perspective.

Keywords
Generative adversarial networks Generative design Design tools
Published
2021-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-78448-5_5
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