Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

Matias Valdenegro-Toro

Keywords: uncertainty quantification, bayesian deep learning, calibration, out of distribution detection

TL;DR: We evaluate uncertainty methods as the training set size is varied, finding that this often breaks assumptions as uncertainty does not always work as expected.

Abstract:

Uncertainty quantification in neural network promises to increase safety of AI systems, but it is not clear how performance might vary with the training set size. In this paper we evaluate seven uncertainty methods on Fashion MNIST and CIFAR10, as we sub-sample and produce varied training set sizes. We find that calibration error and out of distribution detection performance strongly depend on the training set size, with most methods being miscalibrated on the test set with small training sets. Gradient-based methods seem to poorly estimate epistemic
uncertainty and are the most affected by training set size. We expect our results can guide future research into uncertainty quantification and help practitioners select methods based on their particular available data.