Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

TL;DR

This paper introduces a Gaussian mixture prior in variational autoencoders for unsupervised clustering, addressing over-regularization issues and demonstrating competitive results on standard datasets.

Contribution

It extends VAEs with a Gaussian mixture prior and applies a heuristic to mitigate over-regularization, improving clustering performance and interpretability.

Findings

Clusters are distinct and interpretable

Achieves competitive unsupervised clustering results

Heuristic improves model performance

Abstract

We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results.