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，该模型可学习潜在变量上灵活的树形后验分布。所提出的树形变分自编码器（TreeVAE）能够根据样本的内在特征对其进行层次化划分，揭示数据中的隐藏结构。该模型通过自适应调整其架构，以发现编码潜在变量间依赖关系的最优树结构。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节点解码器提升生成性能。实验表明，TreeVAE能在多种数据集（包括真实图像数据）中揭示数据的潜在聚类结构，并发现不同分组间有意义的层次关系。我们通过实证证明，相较于序列化模型，TreeVAE能提供更具竞争力的对数似然下界。最后，得益于其生成特性，TreeVAE可通过条件采样从已发现的聚类结构生成新样本。