Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means to estimate the uncertainty of the main task prediction without modifying the main task model. To be considered robust, an AuxUE must be capable of maintaining its performance and triggering higher uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to provide robust aleatoric and epistemic uncertainty. However, for vision regression tasks, current AuxUE designs are mainly adopted for aleatoric uncertainty estimates, and AuxUE robustness has not been explored. In this work, we propose a generalized AuxUE scheme for more robust uncertainty quantification on regression tasks. Concretely, to achieve a more robust aleatoric uncertainty estimation, different distribution assumptions are considered for heteroscedastic noise, and Laplace distribution is finally chosen to approximate the prediction error. For epistemic uncertainty, we propose a novel solution named Discretization-Induced Dirichlet pOsterior (DIDO), which models the Dirichlet posterior on the discretized prediction error. Extensive experiments on age estimation, monocular depth estimation, and super-resolution tasks show that our proposed method can provide robust uncertainty estimates in the face of noisy inputs and that it can be scalable to both image-level and pixel-wise tasks. Code is available at https://github.com/ENSTA-U2IS/DIDO .

翻译：不确定性量化对于深度神经网络在现实世界中的部署至关重要。辅助不确定性估计器（AuxUE）是无需修改主任务模型即可估计主任务预测不确定性的最有效方法之一。为了实现鲁棒性，AuxUE必须在遇到分布外输入时保持其性能并触发更高的不确定性，即提供鲁棒的偶然不确定性和认知不确定性。然而，对于视觉回归任务，当前的AuxUE设计主要针对偶然不确定性估计，而AuxUE的鲁棒性尚未被探索。本文提出了一种通用的AuxUE方案，用于回归任务中更鲁棒的不确定性量化。具体而言，为了实现更鲁棒的偶然不确定性估计，我们考虑了异方差噪声的不同分布假设，最终选择拉普拉斯分布来近似预测误差。对于认知不确定性，我们提出了一种名为离散化诱导的狄利克雷后验（DIDO）的新方法，该方法在离散化预测误差上建模狄利克雷后验。在年龄估计、单目深度估计和超分辨率任务上的大量实验表明，我们的方法能够在面对噪声输入时提供鲁棒的不确定性估计，并且可扩展到图像级和像素级任务。代码可在 https://github.com/ENSTA-U2IS/DIDO 获取。