  Uncertainty quantification for deep learning is a challenging open problem.
Bayesian statistics offer a mathematically grounded framework to reason about
uncertainties; however, approximate posteriors for modern neural networks still
require prohibitive computational costs. We propose a family of algorithms
which split the classification task into two stages: representation learning
and uncertainty estimation. We compare four specific instances, where
uncertainty estimation is performed via either an ensemble of Stochastic
Gradient Descent or Stochastic Gradient Langevin Dynamics snapshots, an
ensemble of bootstrapped logistic regressions, or via a number of Monte Carlo
Dropout passes. We evaluate their performance in terms of \emph{selective}
classification (risk-coverage), and their ability to detect out-of-distribution
samples. Our experiments suggest there is limited value in adding multiple
uncertainty layers to deep classifiers, and we observe that these simple
methods strongly outperform a vanilla point-estimate SGD in some complex
benchmarks like ImageNet.
