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.

翻译：本文提出Diffuse-TreeVAE，一种将层次聚类融入去噪扩散概率模型（DDPMs）框架的深度生成模型。该方法通过学习到的基于潜在树VAE结构的根嵌入进行采样生成新图像，随后通过层次路径传播，并利用第二阶段DDPM对每个数据簇进行精细化处理，生成独特的高质量图像。该模型不仅提升了图像清晰度，同时确保生成样本能代表其所属簇，解决了先前基于VAE方法的局限性，推动了基于聚类的生成建模技术发展。