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

Understand

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.