Diffusion models exhibit a distributional simplicity bias, learning pairwise input statistics at linear sample complexity while fourth-order cumulants require cubic complexity unless sharing correlated latent structure.
& Goldt, S.Neural networks trained with SGD learn distributions of increasing complexityinInternational Conference on Machine Learning(2023), 28843–28863 (cit
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A theory of learning data statistics in diffusion models, from easy to hard
Diffusion models exhibit a distributional simplicity bias, learning pairwise input statistics at linear sample complexity while fourth-order cumulants require cubic complexity unless sharing correlated latent structure.