Part I: Generalization of Learning Diffusion Models
2025 Tutorial
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Tutorial: Harnessing Low Dimensionality in Diffusion Models: From Theory to Practice
in
Tutorial: Harnessing Low Dimensionality in Diffusion Models: From Theory to Practice
Abstract
We begin with an introduction to diffusion models, followed by a comprehensive study of their generalization: when and why they can learn low-dimensional target structures, how sample complexity scales with intrinsic data dimension, and how they transition from memorization to generalization. We also introduce a probability flow-based metric to quantify generalization and highlight several intriguing behaviors observed during training.
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