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arXiv Machine Learning By Gwangho Kim, Sungyoon Lee

Memorization and Malign Generalization in Conditional Diffusion Models with Random Features

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The paper studies memorization and generalization in conditional diffusion models using a random-feature conditional score model in a high-dimensional setting. It derives asymptotic expressions for training and test losses, showing that in overparameterized regimes wider models better predict the condition-dependent mean but also exhibit reduced within-condition variance, a behavior termed "malign generalization." The analysis also reveals that more informative conditions cause training samples to be memorized at smaller widths, with experimental validation on U‑Net architectures.

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