Memorization and Malign Generalization in Conditional Diffusion Models with Random Features
The work analyzes conditional diffusion models using a mathematical framework in high-dimensional limits to understand memorization and generalization. It shows that wider models improve prediction of the condition but reduce within-condition variance, termed "malign generalization." Furthermore, more informative conditions cause memorization of training data even at smaller model widths.