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.

本文通过训练具有反映人类知觉不确定性的完整标签分布来解决深度神经网络在泛化和鲁棒性方面存在的问题，并呈现了一个新的基准数据集 CIFAR10H。实验结果表明，基于该数据集的显式训练可以实现深度学习分类器具有人类的不确定性，并提高模型泛化性和抵御对抗攻击能力。