Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
<|reference_start|>Structured Generations: Using Hierarchical Clusters to guide Diffusion Models: 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.<|reference_end|>
@article{goncalves2024structured,
    title={Structured Generations: Using Hierarchical Clusters to guide Diffusion
  Models},
    author={Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E.
  Vogt},
    journal={arXiv preprint arXiv:2407.06124},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06124},
    primaryClass={cs.LG cs.CV}
}