  We study the recently introduced stability training as a general-purpose
method to increase the robustness of deep neural networks against input
perturbations. In particular, we explore its use as an alternative to data
augmentation and validate its performance against a number of distortion types
and transformations including adversarial examples. In our image classification
experiments using ImageNet data stability training performs on a par or even
outperforms data augmentation for specific transformations, while consistently
offering improved robustness against a broader range of distortion strengths
and types unseen during training, a considerably smaller hyperparameter
dependence and less potentially negative side effects compared to data
augmentation.
