We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN, while maintaining competitive accuracy.

论文提出了一种通过单次前向传递来训练确定性深度模型的方法，该深度模型可以找到并拒绝分布外的数据点。此外，论文还提出了一种基于确定性不确定性量化（DUQ）的建议，并描述了一种新的损失函数和质心更新策略。通过引入梯度惩罚，我们能够可靠地检测分布外的数据，并且可以在大型数据集上缩放不确定性量化。最后，作者在一些难以检测的数据集上，如FashionMNIST vs. MNIST和CIFAR-10 vs. SVHN，使用单个模型得出了比Deep Ensembles更好的结果。

使用单个深度确定神经网络进行不确定性估计