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 example... (read more)

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