  Data augmentation is a popular pre-processing trick to improve generalization
accuracy. It is believed that by processing augmented inputs in tandem with the
original ones, the model learns a more robust set of features which are shared
between the original and augmented counterparts. However, we show that is not
the case even for the best augmentation technique. In this work, we take a
Domain Generalization viewpoint of augmentation based methods. This new
perspective allowed for probing overfitting and delineating avenues for
improvement. Our exploration with the state-of-art augmentation method provides
evidence that the learned representations are not as robust even towards
distortions used during training. This suggests evidence for the untapped
potential of augmented examples.
