论文标题
使用单个深层确定性神经网络估计不确定性
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
论文作者
论文摘要
我们提出了一种培训确定性深模型的方法，该模型可以在测试时间以单个前向传球从分配数据点中找到并拒绝。我们的方法，确定性的不确定性量化（DUQ）基于RBF网络的思想。我们通过新颖的损耗功能和质心更新方案来扩展这些训练，并匹配SoftMax模型的准确性。通过使用梯度惩罚来实施输入中的变化可检测性，我们可以可靠地检测到分发数据。我们的不确定性量化范围很好地缩放到大型数据集，并且使用单个模型，我们可以改进或匹配深度合奏，以在显着的困难数据集对中，例如FashionMnist与MNIST，以及CIFAR-10和CIFAR-10与SVHH。
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 and match the accuracy of softmax models. 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 in out of distribution detection on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN.
