title: Offline Deep models calibration with bayesian neural networks
abstract: We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep
  neural networks. We show that, even with the most basic amortized approximate
  posterior distribution, and fast fully connected neural network for the likelihood,
  the Bayesian framework clearly outperforms other simple maximum likelihood
  based solutions that have recently shown very good performance, as temperature
  scaling. As an example, we reduce the Expected Calibration
  Error (ECE) from 0.52 to 0.24 on CIFAR-10 and from 4.28 to 2.456 on CIFAR-100
  on two Wide ResNet with 96.13% and 80.39% accuracy respectively, which are
  among the best results published for this task. We demonstrate our robustness and
  performance with experiments on a wide set of state-of-the-art computer vision
  models. Moreover, our approach acts off-line, and thus can be applied to any
  probabilistic model regardless of the limitations that the model may present during
  training. This make it suitable to calibrate systems that make use of pre-trained
  deep neural networks that are expensive to train for a specific task, or to directly
  train a calibrated deep convolutional model with Monte Carlo Dropout approximations, among others. However,
  our method is still complementary with any Bayesian Neural Network for further
  improvement.
authors: Juan Maroñas Roberto Paredes Daniel Ramos
hIndex: Roberto Paredes 23