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- W3171029090 abstract "Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propose a novel and straightforward approach to estimate prediction uncertainty in a pre-trained neural network model. Our method estimates the training data density in representation space for a novel input. A neural network model then uses this information to determine whether we expect the pre-trained model to make a correct prediction. This uncertainty model is trained by predicting in-distribution errors, but can detect out-of-distribution data without having seen any such example. We test our method for a state-of-the art image classification model in the settings of both in-distribution uncertainty estimation as well as out-of-distribution detection." @default.
- W3171029090 created "2021-06-22" @default.
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- W3171029090 date "2020-01-01" @default.
- W3171029090 modified "2023-09-30" @default.
- W3171029090 title "Density Estimation in Representation Space to Predict Model Uncertainty" @default.
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- W3171029090 doi "https://doi.org/10.1007/978-3-030-62144-5_7" @default.
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