A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration

Abstract

We propose a novel Bayesian decision theoretic deep-neural-network (DNN) framework for image segmentation, enabling us to define a principled measure of uncertainty associated with label probabilities. Our framework estimates uncertainty analytically at test time, unlike the state of the art that relies on approximate and expensive algorithms using sampling or multiple passes. Moreover, our framework leads to a novel Bayesian interpretation of the softmax layer. We propose a novel method to improve DNN calibration. Results on three large datasets show that our framework improves segmentation quality and calibration, and provides more realistic uncertainty estimates, over existing methods.