Title: Unsupervised Discovery of Visual Attributes Using Non-Gaussianity
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Unsupervised Representation Learning, Non-Gaussianity, Independent Component Analysis, Latent Space Disentanglement, StyleGAN, Visual Attribute Discovery

Abstract: In everyday language, we often describe a face using attributes such as “young male with glasses, smiling.” In this paper, we ask: can such attributes be discovered automatically from a large dataset of unlabeled images? We show that by exploiting measures of non-Gaussianity, the answer is largely positive. Specifically, we search for linear projections of a StyleGAN latent space whose distributions deviate most strongly from Gaussianity. Our experiments on datasets of faces, churches, and cars demonstrate that these non-Gaussian directions often align with meaningful visual attributes, whereas the original latent coordinates—or even their PCA projections—do not. Taken together, our results suggest that concepts such as “smiling” or “glasses” correspond to statistical properties of large image collections that can be uncovered automatically.