Generative Deep Learning in Architectural Design

Abstract

Generative Adversarial Networks (GANs) are an emerging research area in deep learning that have demonstrated impressive abilities to synthesize designs, however, their application in architectural design has been limited. This research provides a survey of GAN technologies and contributes new knowledge on their application in select architectural design tasks involving the creation and analysis of 2D and 3D designs from specific architectural styles. Experimental results demonstrate how the curation of training data can be used to control the fidelity and diversity of generated designs. Techniques for working with small training sets are introduced and shown to improve the visual quality of synthesized designs. Lastly, experiments demonstrate how GANs might be used analytically to gain insight into specific architectural oeuvres.