Uncertainty Quantification in Deep Neural Networks through Statistical
  Inference on Latent Space
Luigi Sbailò Luca M. Ghiringhelli
Uncertainty-quantification methods are applied to estimate the confidence of
deep-neural-networks classifiers over their predictions. However, most widely
used methods are known to be overconfident. We address this problem by
developing an algorithm that exploits the latent-space representation of data
points fed into the network, to assess the accuracy of their prediction. Using
the latent-space representation generated by the fraction of training set that
the network classifies correctly, we build a statistical model that is able to
capture the likelihood of a given prediction. We show on a synthetic dataset
that commonly used methods are mostly overconfident. Overconfidence occurs also
for predictions made on data points that are outside the distribution that
generated the training data. In contrast, our method can detect such
out-of-distribution data points as inaccurately predicted, thus aiding in the
automatic detection of outliers.