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]
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