Poster
in
Workshop: Challenges in Deployable Generative AI
Tree Variational Autoencoders
Laura Manduchi · Moritz Vandenhirtz · Alain Ryser · Julia Vogt
Keywords: [ Representation Learning ] [ VAE ] [ hierarchical clustering ] [ deep clustering ] [ hierarchical VAE ]
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 structures in the data. It adapts its architecture to discover the optimal tree for encoding dependencies between latent variables, improving generative performance. We show that TreeVAE uncovers underlying clusters in the data and finds meaningful hierarchical relations between the different groups on several datasets. Due to its generative nature, TreeVAE can generate new samples from the discovered clusters via conditional sampling.