Wavelet Score Models Show Local Interactions Drive Diffusion Denoising
Emma Finn argues that the memorization puzzle in diffusion models can be probed by replacing a black-box score network with an analytically solvable wavelet parameterization. In her Microsoft Research New England seminar, Finn presents the method as a way to isolate which data moments and dependency structures matter across noise scales. Her reported experiments on MNIST suggest that local same-scale wavelet interactions improve denoising more consistently than independent coefficient models or orientation-only coupling, while the larger question of whether the framework explains generative novelty remains unresolved.
Microsoft Research·May 26, 2026·12 min read
