  This paper introduces Diffuse-TreeVAE, a deep generative model that
integrates hierarchical clustering into the framework of Denoising Diffusion
Probabilistic Models (DDPMs). The proposed approach generates new images by
sampling from a root embedding of a learned latent tree VAE-based structure, it
then propagates through hierarchical paths, and utilizes a second-stage DDPM to
refine and generate distinct, high-quality images for each data cluster. The
result is a model that not only improves image clarity but also ensures that
the generated samples are representative of their respective clusters,
addressing the limitations of previous VAE-based methods and advancing the
state of clustering-based generative modeling.
