  In this paper, we introduce the concept of Prior Activation Distribution
(PAD) as a versatile and general technique to capture the typical activation
patterns of hidden layer units of a Deep Neural Network used for classification
tasks. We show that the combined neural activations of such a hidden layer have
class-specific distributional properties, and then define multiple statistical
measures to compute how far a test sample's activations deviate from such
distributions. Using a variety of benchmark datasets (including MNIST, CIFAR10,
Fashion-MNIST & notMNIST), we show how such PAD-based measures can be used,
independent of any training technique, to (a) derive fine-grained uncertainty
estimates for inferences; (b) provide inferencing accuracy competitive with
alternatives that require execution of the full pipeline, and (c) reliably
isolate out-of-distribution test samples.
