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dc.contributor.authorJeong, Hyun
dc.contributor.authorYoo, Youngjin
dc.contributor.authorKim, Youngchae
dc.contributor.authorCha, SeungHyun
dc.contributor.authorLee, Jin-Kook
dc.date.accessioned2025-03-08T04:42:21Z
dc.date.available2025-03-08T04:42:21Z
dc.date.issued2023
dc.date.submitted2024-04-02T15:44:31Z
dc.identifierONIX_20240402_9791221502893_10
dc.identifier2704-5846
dc.identifierhttps://library.oapen.org/handle/20.500.12657/89041
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/179869
dc.description.abstractThis 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
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.rightsopen access
dc.subject.otherDesign Style of Architects
dc.subject.otherGenerative AI
dc.subject.otherImage Generation
dc.subject.otherFine-tuning
dc.subject.otherthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
dc.titleChapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style
dc.typechapter
oapen.identifier.doi10.36253/979-12-215-0289-3.91
oapen.relation.isPublishedBy2ec4474d-93b1-4cfa-b313-9c6019b51b1a
oapen.relation.isbn9791221502893
oapen.pages9
oapen.place.publicationFlorence
dc.seriesnumber137


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