  The classification performance of deep neural networks has begun to asymptote
at near-perfect levels. However, their ability to generalize outside the
training set and their robustness to adversarial attacks have not. In this
paper, we make progress on this problem by training with full label
distributions that reflect human perceptual uncertainty. We first present a new
benchmark dataset which we call CIFAR10H, containing a full distribution of
human labels for each image of the CIFAR10 test set. We then show that, while
contemporary classifiers fail to exhibit human-like uncertainty on their own,
explicit training on our dataset closes this gap, supports improved
generalization to increasingly out-of-training-distribution test datasets, and
confers robustness to adversarial attacks.
