  The Adversarially Learned Mixture Model (AMM) is a generative model for
unsupervised or semi-supervised data clustering. The AMM is the first
adversarially optimized method to model the conditional dependence between
inferred continuous and categorical latent variables. Experiments on the MNIST
and SVHN datasets show that the AMM allows for semantic separation of complex
data when little or no labeled data is available. The AMM achieves a
state-of-the-art unsupervised clustering error rate of 2.86% on the MNIST
dataset. A semi-supervised extension of the AMM yields competitive results on
the SVHN dataset.
