Convergence of score-based generative modeling for general data distributionsDownload PDF

Published: 29 Nov 2022, Last Modified: 05 May 2023SBM 2022 PosterReaders: Everyone
Keywords: score-based generative modelling, diffusion model, reverse SDE, minimal data assumptions
TL;DR: Polynomial convergence results for denoising diffusion models with minimal data assumptions
Abstract: We give polynomial convergence guarantees for denoising diffusion models that do not rely on the data distribution satisfying functional inequalities or strong smoothness assumptions. Assuming a $L^2$-accurate score estimate, we obtain Wasserstein distance guarantees for any distributions of bounded support or sufficiently decaying tails, as well as TV guarantees for distributions with further smoothness assumptions.
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