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Learning for Single-Shot Confidence Calibration in Deep Neural Networks through Stochastic Inferences
[article]
2019
arXiv
pre-print
We propose a generic framework to calibrate accuracy and confidence of a prediction in deep neural networks through stochastic inferences. We interpret stochastic regularization using a Bayesian model, and analyze the relation between predictive uncertainty of networks and variance of the prediction scores obtained by stochastic inferences for a single example. Our empirical study shows that the accuracy and the score of a prediction are highly correlated with the variance of multiple
arXiv:1809.10877v5
fatcat:bq7hw5si4fd6vp4i2qjdcaq2lm