Convergence of score-based generative modeling for general data distributions

• URL: http://arxiv.org/abs/2209.12381v1
• Date: Mon, 26 Sep 2022 02:38:36 GMT
• Title: Convergence of score-based generative modeling for general data distributions
• Authors: Holden Lee, Jianfeng Lu, Yixin Tan
• Abstract summary: We give convergence guarantees for denoising diffusion models that do not rely on the data distribution satisfying functional inequalities or strong smoothness assumptions. 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.
• Score: 9.953088581242845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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