  Data augmentation is a commonly applied technique with two seemingly related
advantages. With this method one can increase the size of the training set
generating new samples and also increase the invariance of the network against
the applied transformations. Unfortunately all images contain both relevant and
irrelevant features for classification therefore this invariance has to be
class specific. In this paper we will present a new method which uses saliency
maps to restrict the invariance of neural networks to certain regions,
providing higher test accuracy in classification tasks.
