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Convergence of denoising diffusion models under the manifold hypothesis
[article]
2022
arXiv
pre-print
Denoising diffusion models are a recent class of generative models exhibiting state-of-the-art performance in image and audio synthesis. Such models approximate the time-reversal of a forward noising process from a target distribution to a reference density, which is usually Gaussian. Despite their strong empirical results, the theoretical analysis of such models remains limited. In particular, all current approaches crucially assume that the target density admits a density w.r.t. the Lebesgue
arXiv:2208.05314v1
fatcat:hqg3vhfcpvalfhn4uvwo6kbiqq