InfoDiffusion: Representation Learning Using Information Maximizing
  Diffusion Models
Yingheng Wang Yair Schiff Aaron Gokaslan Weishen Pan Fei Wang Christopher De Sa Volodymyr Kuleshov
While diffusion models excel at generating high-quality samples, their latent
variables typically lack semantic meaning and are not suitable for
representation learning. Here, we propose InfoDiffusion, an algorithm that
augments diffusion models with low-dimensional latent variables that capture
high-level factors of variation in the data. InfoDiffusion relies on a learning
objective regularized with the mutual information between observed and hidden
variables, which improves latent space quality and prevents the latents from
being ignored by expressive diffusion-based decoders. Empirically, we find that
InfoDiffusion learns disentangled and human-interpretable latent
representations that are competitive with state-of-the-art generative and
contrastive methods, while retaining the high sample quality of diffusion
models. Our method enables manipulating the attributes of generated images and
has the potential to assist tasks that require exploring a learned latent space
to generate quality samples, e.g., generative design.