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Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Neural Information Processing Systems
Equipped with the knowledge of sample difficulty, we further propose to use it for regularizing the prediction confidence.
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Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan
Neural Information Processing Systems
CIFAR-10) and tested on another (e.g.
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