Adversarially Learned Mixture Model

Andrew Jesson, Cécile Low-Kam, Tanya Nair, Florian Soudan, Florent Chandelier, Nicolas Chapados

Abstract: 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 unsupervised clustering error rates of 3.32% and 20.4% on the MNIST and SVHN datasets, respectively. A semi-supervised extension of the AMM achieves a classification error rate of 5.60% on the SVHN dataset.

Keywords: Unsupervised, Semi-supervised, Generative, Adversarial, Clustering

TL;DR: The AMM is the first fully adversarially optimized method to model the conditional dependence between categorical and continuous latent variables.