  Deep clustering (DC) leverages the representation power of deep architectures
to learn embedding spaces that are optimal for cluster analysis. This approach
filters out low-level information irrelevant for clustering and has proven
remarkably successful for high dimensional data spaces. Some DC methods employ
Generative Adversarial Networks (GANs), motivated by the powerful latent
representations these models are able to learn implicitly. In this work, we
propose HC-MGAN, a new technique based on GANs with multiple generators
(MGANs), which have not been explored for clustering. Our method is inspired by
the observation that each generator of a MGAN tends to generate data that
correlates with a sub-region of the real data distribution. We use this
clustered generation to train a classifier for inferring from which generator a
given image came from, thus providing a semantically meaningful clustering for
the real distribution. Additionally, we design our method so that it is
performed in a top-down hierarchical clustering tree, thus proposing the first
hierarchical DC method, to the best of our knowledge. We conduct several
experiments to evaluate the proposed method against recent DC methods,
obtaining competitive results. Last, we perform an exploratory analysis of the
hierarchical clustering tree that highlights how accurately it organizes the
data in a hierarchy of semantically coherent patterns.

Machine Learning;Computer Vision and Pattern Recognition