On Calibrated Model Uncertainty in Deep Learning

TL;DR

This paper enhances Bayesian neural networks by calibrating uncertainty estimates to improve decision-making in high-stakes tasks like Covid-19 diagnosis, without sacrificing accuracy.

Contribution

It introduces a loss-calibrated Bayesian inference method for dropweights neural networks and proposes MUCE for measuring calibration quality.

Findings

Reduces miscalibration significantly in models

Improves diagnostic reliability without accuracy loss

Enhances detection of Covid-19 from X-Ray images

Abstract

Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approximate inference for the loss-calibrated Bayesian framework to dropweights based Bayesian neural networks by maximising expected utility over a model posterior to calibrate uncertainty in deep learning. Furthermore, we show that decisions informed by loss-calibrated uncertainty can improve diagnostic performance to a greater extent than straightforward alternatives. We propose Maximum Uncertainty Calibration Error (MUCE) as a metric to measure calibrated confidence, in addition to its prediction especially for high-risk applications, where the goal is to minimise the worst-case deviation between error and estimated uncertainty. In experiments, we show the correlation between error in prediction and estimated uncertainty by interpreting Wasserstein distance as the accuracy of prediction. We evaluated the effectiveness of our approach to detecting Covid-19 from X-Ray images. Experimental results show that our method reduces miscalibration considerably, without impacting the models accuracy and improves reliability of computer-based diagnostics.