Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a divergence function and the posterior. Current methods such as Deep Ensembles or MC dropout underperform at estimating the epistemic uncertainty, since they primarily consider the posterior when sampling models. We suggest Quantification of Uncertainty with Adversarial Models (QUAM) to better estimate the epistemic uncertainty. QUAM identifies regions where the whole product under the integral is large, not just the posterior. Consequently, QUAM has lower approximation error of the epistemic uncertainty compared to previous methods. Models for which the product is large correspond to adversarial models (not adversarial examples!). Adversarial models have both a high posterior as well as a high divergence between their predictions and that of a reference model. Our experiments show that QUAM excels in capturing epistemic uncertainty for deep learning models and outperforms previous methods on challenging tasks in the vision domain.

翻译：量化不确定性对于在现实世界应用中做出可操作预测至关重要。预测不确定性量化的一个关键部分是认知不确定性的估计，其定义为散度函数与后验概率乘积的积分。现有方法（如深度集成或MC dropout）在估计认知不确定性方面表现欠佳，主要原因是它们在采样模型时主要考虑后验概率。我们提出对抗模型不确定性量化（QUAM）方法以更好地估计认知不确定性。QUAM能够识别积分中整个乘积较大的区域，而不仅仅是后验概率较大的区域。因此，相较于先前方法，QUAM对认知不确定性的近似误差更小。乘积较大的模型对应对抗模型（注意：非对抗样本！）。对抗模型同时具有高后验概率以及其预测与参考模型预测之间的高散度。实验表明，QUAM在捕获深度学习模型认知不确定性方面表现出色，并在视觉领域的挑战性任务上优于现有方法。