MPCC: Matching Priors and Conditionals for Clustering

Nicolás Astorga, Pablo Huijse, Pavlos Protopapas, Pablo Estévez ;


Clustering is a fundamental task in unsupervised learning that depends heavily on the data representation that is used. Deep generative models have appeared as a promising tool to learn informative low-dimensional data representations. We propose Matching Priors and Conditionals for Clustering (MPCC), a GAN-based model with an encoder to infer latent variables and cluster categories from data, and a flexible decoder to generate samples from a conditional latent space. With MPCC we demonstrate that a deep generative model can be competitive/superior against discriminative methods in clustering tasks surpassing the state of the art over a diverse set of benchmark datasets. Our experiments show that adding a learnable prior and augmenting the number of encoder updates improve the quality of the generated samples, obtaining an inception score of $9.49 \pm 0.15$ and improving the Fr\'echet inception distance over the state of the art by a $46.9\%$ in CIFAR10."

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