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.

翻译：我们提出了一种新的生成式层次聚类模型，该模型能够学习潜在变量上灵活的基于树的后验分布。所提出的树变分自编码器（TreeVAE）根据样本的内在特征对其进行层次划分，从而揭示数据中的隐藏结构。它通过自适应调整架构来发现编码潜在变量间依赖关系的最优树结构。所提出的基于树的生成架构支持轻量级条件推理，并通过利用专门的叶解码器提升生成性能。我们证明，TreeVAE能够发现数据中的潜在集群，并在多个数据集（包括真实成像数据）上找到不同分组之间有意义的层次关系。实验表明，与序列化变分自编码器相比，TreeVAE提供了更具竞争力的对数似然下界。最后，由于其生成特性，TreeVAE能够通过条件采样从所发现的集群中生成新的样本。