  We propose an algorithm combining calibrated prediction and generalization
bounds from learning theory to construct confidence sets for deep neural
networks with PAC guarantees---i.e., the confidence set for a given input
contains the true label with high probability. We demonstrate how our approach
can be used to construct PAC confidence sets on ResNet for ImageNet, a visual
object tracking model, and a dynamics model for the half-cheetah reinforcement
learning problem.
