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This paper addresses the problem of selective classification for deep neural networks, where a model is allowed to abstain from low-confidence predictions to avoid potential errors.
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A Survey of Uncertainty in Deep Neural Networks, January 2022
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Scaling Out-of-Distribution Detection for Real-World Settings
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