  Deep neural networks (DNNs) have achieved state-of-the-art results in various
pattern recognition tasks. However, they perform poorly on out-of-distribution
adversarial examples i.e. inputs that are specifically crafted by an adversary
to cause DNNs to misbehave, questioning the security and reliability of
applications. In this paper, we encourage DNN classifiers to learn more
discriminative features by imposing a center loss in addition to the regular
softmax cross-entropy loss. Intuitively, the center loss encourages DNNs to
simultaneously learns a center for the deep features of each class, and
minimize the distances between the intra-class deep features and their
corresponding class centers. We hypothesize that minimizing distances between
intra-class features and maximizing the distances between inter-class features
at the same time would improve a classifier's robustness to adversarial
examples. Our results on state-of-the-art architectures on MNIST, CIFAR-10, and
CIFAR-100 confirmed that intuition and highlight the importance of
discriminative features.
