Aram-Alexandre Pooladian

Low Intrinsic Dimension Lets Blind Denoisers Track Implicit Diffusion Schedules

Aram-Alexandre Pooladian argues that blind denoising diffusion models can dispense with an explicit noise schedule because the noisy sample can reveal its own noise level when the data are low-dimensional inside a high-dimensional ambient space. In work with Zahra Kadkhodaie, Sinho Chewi, and Eero Simoncelli, he presents theory and experiments showing that such models can track an implicit reverse-process schedule and sample accurately in polynomially many steps as a function of intrinsic dimension. The empirical comparison suggests a further advantage: blind models may avoid finite-step mismatch between a prescribed schedule and the actual residual noise in the sample.