  Recent advances in deep learning have shown their ability to learn strong
feature representations for images. The task of image clustering naturally
requires good feature representations to capture the distribution of the data
and subsequently differentiate data points from one another. Often these two
aspects are dealt with independently and thus traditional feature learning
alone does not suffice in partitioning the data meaningfully. Variational
Autoencoders (VAEs) naturally lend themselves to learning data distributions in
a latent space. Since we wish to efficiently discriminate between different
clusters in the data, we propose a method based on VAEs where we use a Gaussian
Mixture prior to help cluster the images accurately. We jointly learn the
parameters of both the prior and the posterior distributions. Our method
represents a true Gaussian Mixture VAE. This way, our method simultaneously
learns a prior that captures the latent distribution of the images and a
posterior to help discriminate well between data points. We also propose a
novel reparametrization of the latent space consisting of a mixture of discrete
and continuous variables. One key takeaway is that our method generalizes
better across different datasets without using any pre-training or learnt
models, unlike existing methods, allowing it to be trained from scratch in an
end-to-end manner. We verify our efficacy and generalizability experimentally
by achieving state-of-the-art results among unsupervised methods on a variety
of datasets. To the best of our knowledge, we are the first to pursue image
clustering using VAEs in a purely unsupervised manner on real image datasets.
