Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences
Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences
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 …