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Diffusion Models: Theory Explains Generalization and Memorization

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Researchers have developed a theoretical framework to understand generalization and memorization in diffusion models. Their work derives precise expressions for test and train errors in Denoising Score Matching (DSM) using random feature neural networks. The study identifies distinct regimes of generalization and memorization based on dataset size, model complexity, and the number of noise samples used during training, aligning with empirical observations. AI

RANK_REASON This is a research paper published on arXiv detailing theoretical insights into diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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Diffusion Models: Theory Explains Generalization and Memorization

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This is a research paper published on arXiv detailing theoretical insights into diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Anand Jerry George, Rodrigo Veiga, Nicolas Macris ·

    Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves

    arXiv:2502.00336v3 Announce Type: replace-cross Abstract: We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity and the size of the training dataset. In …

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