2022
DOI: 10.52842/conf.ecaade.2022.2.621
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Deep Learning Spatial Signature - Inverted GANs for Isovist representation in architectural floorplan

Abstract: The advances of Generative Adversarial Networks (GANs) have provided a new experimental ground for creative architecture processes. However, the analytical potential of the latent representation of GANs is yet to be explored for architectural spatial analysis. Furthermore, most research on GANs for floorplan learning in architecture uses images as its main representation medium. This paper presents an experimental framework that uses one-dimensional periodic isovist samples and GANs inversion to recover its la… Show more

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