This paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.


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    Titel :

    Exploration of Campus Layout Based on Generative Adversarial Network


    Untertitel :

    Discussing the Significance of Small Amount Sample Learning for Architecture


    Beteiligte:
    Yuan, Philip F. (Herausgeber:in) / Yao, Jiawei (Herausgeber:in) / Yan, Chao (Herausgeber:in) / Wang, Xiang (Herausgeber:in) / Leach, Neil (Herausgeber:in) / Liu, Yubo (Autor:in) / Luo, Yihua (Autor:in) / Deng, Qiaoming (Autor:in) / Zhou, Xuanxing (Autor:in)

    Kongress:

    The International Conference on Computational Design and Robotic Fabrication ; 2020 ; Shanghai, China July 05, 2020 - July 06, 2020


    Erschienen in:

    Erscheinungsdatum :

    29.01.2021


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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