Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering in tensorflow. The model is based on the M2 Unsupervised model proposed by Kingma et al. for semi-supervised learning. Unlike other implementations that use marginalization for the categorical latent variable, we use the Gumbel-Softmax distribution, resulting in better time complexity because of the reduced number of gradient estimations. We modified the M2 generative model to represent a Mixture of Gaussians.

1. PyTorch. We tested our method with the 1.3.0 pytorch version. You can Install PyTorch by following the instructions on its website: https://pytorch.org/get-started/locally/.

2. Python 3.6.8. We implemented our method with the 3.6.8 version. Additional libraries include: numpy, scipy and matplotlib.