Unveiling the Dynamics of Generative Diffusion Models: A Machine Learning Approach to Understanding Data Structures and Dimensionality

The recent advancements in machine learning, particularly in generative models, have been marked by the emergence of diffusion models (DMs) as powerful tools for modeling complex data distributions and generating realistic samples across various domains such as images, videos, audio, and 3D scenes. Despite their practical success, the full theoretical understanding of generative diffusion models […]

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