  Deep neural networks are capable of training fast and generalizing well
within many domains. Despite their promising performance, deep networks have
shown sensitivities to perturbations of their inputs (e.g., adversarial
examples) and their learned feature representations are often difficult to
interpret, raising concerns about their true capability and trustworthiness.
Recent work in adversarial training, a form of robust optimization in which the
model is optimized against adversarial examples, demonstrates the ability to
improve performance sensitivities to perturbations and yield feature
representations that are more interpretable. Adversarial training, however,
comes with an increased computational cost over that of standard (i.e.,
nonrobust) training, rendering it impractical for use in large-scale problems.
Recent work suggests that a fast approximation to adversarial training shows
promise for reducing training time and maintaining robustness in the presence
of perturbations bounded by the infinity norm. In this work, we demonstrate
that this approach extends to the Euclidean norm and preserves the
human-aligned feature representations that are common for robust models.
Additionally, we show that using a distributed training scheme can further
reduce the time to train robust deep networks. Fast adversarial training is a
promising approach that will provide increased security and explainability in
machine learning applications for which robust optimization was previously
thought to be impractical.
