  Street architectures play an essential role in city image and streetscape
analysing. However, existing approaches are all supervised which require costly
labeled data. To solve this, we propose a street architectural unsupervised
classification framework based on Information maximizing Generative Adversarial
Nets (InfoGAN), in which we utilize the auxiliary distribution $Q$ of InfoGAN
as an unsupervised classifier. Experiments on database of true street view
images in Nanjing, China validate the practicality and accuracy of our
framework. Furthermore, we draw a series of heuristic conclusions from the
intrinsic information hidden in true images. These conclusions will assist
planners to know the architectural categories better.
