Calibration of deep probabilistic models with decoupled bayesian neural networks

Abstract

[EN] Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well -calibrated, seriously limiting their use in critical decision scenarios. In this work, we propose to use a decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to map the uncalibrated probabilities provided by a DNN to calibrated ones, consistently improving calibration. Our results evi-dence that incorporating uncertainty provides more reliable probabilistic models, a critical condition for achieving good calibration. We report a generous collection of experimental results using high -accuracy DNNs in standardized image classification benchmarks, showing the good performance, flexibil-ity and robust behaviour of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided. (C) 2020 Elsevier B.V. All rights reserved.