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This paper applies state-of-the-art techniques in deep learning and computervision to measure visual similarities between architectural designs bydifferent architects. Using a dataset consisting of web scraped images and anoriginal collection of images of architectural works, we first train a deepconvolutional neural network (DCNN) model capable of achieving 73% accuracy inclassifying works belonging to 34 different architects. Through examining theweights in the trained DCNN model, we are able to quantitatively measure thevisual similarities between architects that are implicitly learned by ourmodel. Using this measure, we cluster architects that are identified to besimilar and compare our findings to conventional classification made byarchitectural historians and theorists. Our clustering of architectural designsremarkably corroborates conventional views in architectural history, and thelearned architectural features also coheres with the traditional understandingof architectural designs.

Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence