  We propose a prototype-based approach for improving explainability of softmax
classifiers that provides an understandable prediction confidence, generated
through stochastic sampling of prototypes, and demonstrates potential for out
of distribution detection (OOD). By modifying the model architecture and
training to make predictions using similarities to any set of class examples
from the training dataset, we acquire the ability to sample for prototypical
examples that contributed to the prediction, which provide an instance-based
explanation for the model's decision. Furthermore, by learning relationships
between images from the training dataset through relative distances within the
model's latent space, we obtain a metric for uncertainty that is better able to
detect out of distribution data than softmax confidence.
