  Recently, there has been increasing interest to leverage the competence of
neural networks to analyze data. In particular, new clustering methods that
employ deep embeddings have been presented. In this paper, we depart from
centroid-based models and suggest a new framework, called Clustering-driven
deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering
using a neural network. We present a clustering-driven embedding based on a
Siamese network that encourages pairs of data points to output similar
representations in the latent space. Our pair-based model allows augmenting the
information with labeled pairs to constitute a semi-supervised framework. Our
approach is based on analyzing the losses associated with each pair to refine
the set of constraints. We show that clustering performance increases when
using this scheme, even with a limited amount of user queries. We demonstrate
how our architecture is adapted for various types of data and present the first
deep framework to cluster 3D shapes.
