Dirichlet Variational Autoencoder

•This paper is a study on Dirichlet prior in variational autoencoder.•Our model outperforms baseline variational autoencoders in the perspective of loglikelihood.•Our model produces more meaningful and interpretable latent representation with no component collapsing compared to baseline variational autoehcoders.•Our model achieves the best classification accuracy in the (semi-)supervised classification tasks compared to baseline variational autoencoders.•Our model shows better performances in topic model augmentation.