Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors

arXiv:2401.02739v2 Announce Type: replace
Abstract: We propose denoising diffusion variational inference (DDVI), an approximate inference algorithm for latent variable models which relies on diffusion models as flexible variational posteriors. Specifically, our method introduces an expressive class of approximate posteriors with auxiliary latent variables that perform diffusion in latent space by reversing a user-specified noising process. We fit these models by optimizing a lower bound on the marginal likelihood inspired by the wake-sleep algorithm. Our method is easy to implement (it …

abstract algorithm approximate inference arxiv class cs.lg denoising diffusion diffusion models inference q-bio.qm space stat.ml type variables