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.
@article{arxiv.1807.05344,
title = {Adversarially Learned Mixture Model},
author = {Andrew Jesson and Cécile Low-Kam and Tanya Nair and Florian Soudan and Florent Chandelier and Nicolas Chapados},
journal= {arXiv preprint arXiv:1807.05344},
year = {2022}
}