To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models.Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility scores for generated data. To make this approach scalable, we propose FLARE (Fisher-Laplace Randomized Estimator), which approximates the Fisher information using a uniformly random subset of model parameters. Empirically, FLARE improves uncertainty estimation in synthetic time-series generation tasks, achieving more accurate and reliable filtering than other methods. Theoretically, we bound the convergence rate of our randomized approximation and provide analytic and empirical evidence that last-layer Laplace approximations are insufficient for this task.

翻译：为确保高质量输出，量化扩散模型的认知不确定性至关重要。现有方法常因混淆认知不确定性与偶然不确定性而不可靠。我们提出一种基于费雪信息的方法，能显式分离认知方差，为生成数据提供更可靠的可信度评分。为实现可扩展性，我们提出FLARE（费雪-拉普拉斯随机估计器），通过均匀随机选取模型参数子集来近似费雪信息。实验表明，FLARE在合成时间序列生成任务中改进了不确定性估计，相比其他方法实现了更精确可靠的滤波。理论上，我们界定了随机化近似的收敛速率，并提供分析和实证证据表明，仅使用最后一层的拉普拉斯近似不足以完成此任务。