Unsupervised Image Categorization Based on Variational Autoencoder and Student’s-T Mixture Model

In this work, a novel generative robust image catego-rization approach is developed based on variational autoencoder (VAE) and Student's-T Mixture Model (STMM). The network structure composed of VAE, STMM and Convolutional Neural Network (CNN) generates data. More specifically, first, a cluster is chosen using the STMM. Then, a latent representation is extracted from the selected cluster through a CNN encoder. After that, an observation is generated based on another CNN through a decoding process. The proposed model is learned through variational inference where the Evidence Lower Bound is optimized according to Stochastic Gradient Descent(SGD) and the reparameterization trick. Based on our experimental results, the proposed generative clustering approach is able to outperform classical clustering approaches (e.g. K-means, Gaussian Mixture Models) and other related generative clustering approaches. Furthermore, we show that our generative model is able to generate highly realistic samples without using any supervised information during training.