Abstract
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community because it is important for deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modeling data distributions in feature spaces. We further incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble stochastic neural networks (BE-SNNs) and overcome the feature collapse problem. We compare the performance of the proposed BE-SNNs with the other state-of-the-art approaches and show that BE-SNNs yield superior performance on several OOD detection benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, Fashion-MNIST vs NotMNIST dataset, and the CIFAR10 vs SVHN dataset.