Diffusion model generalization is explained by the covariance of the noisy score-matching target, which adds state-dependent noise to sampling and fills gaps in the training distribution.
Self-consistent dynamical field theory of kernel evolution in wide neural networks
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Generalization through variance: how noise shapes inductive biases in diffusion models
Diffusion model generalization is explained by the covariance of the noisy score-matching target, which adds state-dependent noise to sampling and fills gaps in the training distribution.