We propose a new generative hierarchical clustering model that learns a
flexible tree-based posterior distribution over latent variables. The proposed
Tree Variational Autoencoder (TreeVAE) hierarchically divides samples according
to their intrinsic characteristics, shedding light on hidden structure in the
data. It adapts its architecture to discover the optimal tree for encoding
dependencies between latent variables. The proposed tree-based generative
architecture permits 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.

提出了一种新的生成分层聚类模型 ——Tree Variational Autoencoder（TreeVAE），它能够分层地划分样本并发现数据中潜在结构，并利用树形结构中的叶子节点解码器提高了生成性能。TreeVAE 在不同的数据集上表现出了较高的对数似然下界，并且通过条件采样能够从聚类中生成新的样本。