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In this paper, we introduce the concept of Prior Activation Distribution(PAD) as a versatile and general technique to capture the typical activationpatterns of hidden layer units of a Deep Neural Network used for classificationtasks. We show that the combined neural activations of such a hidden layer haveclass-specific distributional properties, and then define multiple statisticalmeasures to compute how far a test sample's activations deviate from suchdistributions. 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 uncertaintyestimates for inferences; (b) provide inferencing accuracy competitive withalternatives that require execution of the full pipeline, and (c) reliablyisolate out-of-distribution test samples.

Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units