Use an aggressive variant of MixUp (Zhang et al.). This is done by sampling the alpha parameter from a uniform distribution instead of a beta distribution. MixUp is used here in order to help the student model capture the function underlying the teacher model. MixUp linearly interpolates between different samples across the data manifold. So the rationale here is if the student is trained to fit that it should be able to match the teacher model better. To incorporate more invariance MixUp is coupled with \"Inception-style\" cropping (Szegedy et al.). This is where the \"function matching\" term makes its way in the original paper.