Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.
Maximum Mean Discrepancy (MMD), proposed by Gretton et al
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Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.