  We propose Tree Variational Autoencoder (TreeVAE), a new generative
hierarchical clustering model that learns a flexible tree-based posterior
distribution over latent variables. TreeVAE hierarchically divides samples
according to their intrinsic characteristics, shedding light on hidden
structures in the data. It adapts its architecture to discover the optimal tree
for encoding dependencies between latent variables. The proposed tree-based
generative architecture enables lightweight conditional inference and improves
generative performance by utilizing specialized leaf decoders. We show that
TreeVAE uncovers underlying clusters in the data and finds meaningful
hierarchical relations between the different groups on a variety of datasets,
including real-world imaging data. We present empirically that TreeVAE provides
a more competitive log-likelihood lower bound than the sequential counterparts.
Finally, due to its generative nature, TreeVAE is able to generate new samples
from the discovered clusters via conditional sampling.
