Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
This addresses the issue of unreliable probabilistic outputs in DNNs for critical decision scenarios, representing an incremental improvement over existing calibration methods.
The paper tackled the problem of poor calibration in deep neural networks (DNNs) by proposing a decoupled Bayesian stage using Bayesian Neural Networks (BNNs) to map uncalibrated probabilities to calibrated ones, consistently improving calibration with experimental results on standardized image classification benchmarks.
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 evidence 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, flexibility and robust behavior of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided.