Deep Generative Clustering with VAEs and Expectation-Maximization

Michael Adipoetra; Ségolène Martin

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arXiv · 2501.07358

Deep Generative Clustering with VAEs and Expectation-Maximization

Michael Adipoetra·Ségolène Martin

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Abstract

We propose a novel deep clustering method that integrates Variational Autoencoders (VAEs) into the Expectation-Maximization (EM) framework. Our approach models the probability distribution of each cluster with a VAE and alternates between updating model parameters by maximizing the Evidence Lower Bound (ELBO) of the log-likelihood and refining cluster assignments based on the learned distributions. This enables effective clustering and generation of new samples from each cluster. Unlike existing VAE-based methods, our approach eliminates the need for a Gaussian Mixture Model (GMM) prior or additional regularization techniques. Experiments on MNIST and FashionMNIST demonstrate superior clustering performance compared to state-of-the-art methods.

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Michael Adipoetra, Ségolène Martin. 2025-01-13. Deep Generative Clustering with VAEs and Expectation-Maximization. https://arxiv.org/abs/2501.07358

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