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- W4375868924 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." @default.
- W4375868924 created "2023-05-10" @default.
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- W4375868924 date "2023-06-04" @default.
- W4375868924 modified "2023-09-27" @default.
- W4375868924 title "Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection" @default.
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- W4375868924 doi "https://doi.org/10.1109/icassp49357.2023.10095503" @default.
- W4375868924 hasPublicationYear "2023" @default.
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