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    Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style

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    Author(s)
    Jeong, Hyun
    Yoo, Youngjin
    Kim, Youngchae
    Cha, SeungHyun
    Lee, Jin-Kook
    Language
    English
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    Abstract
    This paper suggests the potential application of generative artificial intelligence-based image generation technology in the field of architecture, for early phase shape planning, using the styles of renowned architects. The study employed the following approaches: 1) Intensive image generation based on the styles of 20 architects to test the AI's recognition ability and image quality. 2) Additional training was conducted for architects with low recognition rates to construct an enhanced learning model in the quality of image generation. 3) In addition to generating architectural visualization images using existing architects' design styles, alternative styles were proposed through design combinations, aiming to concretize ambiguous idea communication in the early stages of design and enhance its efficiency. The study sheds light on the future prospects of applying this generative AI model in the field of architecture
    URI
    https://library.oapen.org/handle/20.500.12657/89041
    Keywords
    Design Style of Architects; Generative AI; Image Generation; Fine-tuning
    DOI
    10.36253/979-12-215-0289-3.91
    ISBN
    9791221502893, 9791221502893
    Publisher
    Firenze University Press
    Publisher website
    https://www.fupress.com/
    Publication date and place
    Florence, 2023
    Series
    Proceedings e report, 137
    Classification
    Artificial intelligence
    Pages
    9
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/legalcode
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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