GAN as a generative architectural plan layout tool: A case study for training DCGAN with palladian plans and evaluation of DCGAN outputs


Creative Commons License

Uzun C., Çolakoğlu M. B. , İnceoğlu A.

A/Z ITU Journal of the Faculty of Architecture, vol.17, no.2, pp.185-198, 2020 (Refereed Journals of Other Institutions) identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.5505/itujfa.2020.54037
  • Title of Journal : A/Z ITU Journal of the Faculty of Architecture
  • Page Numbers: pp.185-198

Abstract

© 2020, Istanbul Teknik Universitesi, Faculty of Architecture. All rights reserved.This study aims to produce Andrea Palladio’s architectural plan schemes autonomously with generative adversarial networks(GAN), and to evaluate the plan drawing productions of GAN as a generative plan layout tool. GAN is a class of deep neural nets which is a generative model. In deep learning models, repetitive processes can be automated. Architectural drawing is a repetitive process in the act of architecture and plan drawing process can be made automated. For the automation of plan production system we used deep convolutional generative adversarial network (DCGAN) which is a subset of GAN models. And we evaluated the outputs of the DCGAN Palladian Plan scheme productions. Results show that not geometric similarities (shapes), but probabilistic models are at the centre of the generative system of GAN. For this reason, it should be kept in mind that while GAN algorithms are used as a generative system, they will produce statistically close visual models rather than geometrically close models. Nonetheless, GAN can generate both statistically and geometrically close models to the dataset. In first section we introduced a brief description about the place of the drawing in architecture field and future foresight of architecture drawings. In the second section, we gave detailed information about the literature on autonomous plan drawing systems. In the following sections, we explained the methodology of this study and the process of creating the plan drawing dataset, the algorithm training procedure, at the end we evaluated the generated plan schemes with rapid scene categorization and Frechet inception score.