Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data

ArXi:2603.03700v2 Announce Type: replace-cross Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often provide pessimistic convergence rates that do not reflect the intrinsic low-dimensional structure common in real data, such as that arising in natural images. In this work, we study the statistical convergence of score-based diffusion models for learning an unknown distribution $\mu$ from finitely many samples.