Poster
in
Workshop: NeurIPS 2022 Workshop on Score-Based Methods

Convergence of score-based generative modeling for general data distributions

Holden Lee · Jianfeng Lu · Yixin Tan


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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