Google Research

Clustering Meets Implicit Generative Models

  • Francesco Locatello
  • Damien Vincent
  • Ilya Tolstikhin
  • Gunnar Rätsch
  • Sylvain Gelly
  • Bernhard Scholkopf
arXiv (2018)


Clustering is a cornerstone of unsupervised learning which can be thought as disen- tangling the multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we particularize for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work