  In this paper we propose a new augmentation technique, called patch
augmentation, that, in our experiments, improves model accuracy and makes
networks more robust to adversarial attacks. In brief, this data-independent
approach creates new image data based on image/label pairs, where a patch from
one of the two images in the pair is superimposed on to the other image,
creating a new augmented sample. The new image's label is a linear combination
of the image pair's corresponding labels. Initial experiments show a several
percentage point increase in accuracy on CIFAR-10, from a baseline of
approximately 81% to 89%. CIFAR-100 sees larger improvements still, from a
baseline of 52% to 68% accuracy. Networks trained using patch augmentation are
also more robust to adversarial attacks, which we demonstrate using the Fast
Gradient Sign Method.
