A Mixture of Variational Autoencoders for Deep Clustering

Avi Caciularu, Jacob Goldberger

Keywords: deep clustering, variational auto encoder, VAE

Abstract:

In this study, we propose a deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with a multi encoder-decoder neural architecture. This setup enforces a complementary structure that guides the learned latent representations towards a more meaningful space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders.
We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to outperform state-of-the-art deep clustering methods significantly.

One-sentence Summary: A VAE based clustering algorithm based on multiple autoencoders that obtains SOTA results

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