  Mixup is a popular data augmentation technique based on creating new samples
by linear interpolation between two given data samples, to improve both the
generalization and robustness of the trained model. Knowledge distillation
(KD), on the other hand, is widely used for model compression and transfer
learning, which involves using a larger network's implicit knowledge to guide
the learning of a smaller network. At first glance, these two techniques seem
very different, however, we found that "smoothness" is the connecting link
between the two and is also a crucial attribute in understanding KD's interplay
with mixup. Although many mixup variants and distillation methods have been
proposed, much remains to be understood regarding the role of a mixup in
knowledge distillation. In this paper, we present a detailed empirical study on
various important dimensions of compatibility between mixup and knowledge
distillation. We also scrutinize the behavior of the networks trained with a
mixup in the light of knowledge distillation through extensive analysis,
visualizations, and comprehensive experiments on image classification. Finally,
based on our findings, we suggest improved strategies to guide the student
network to enhance its effectiveness. Additionally, the findings of this study
provide insightful suggestions to researchers and practitioners that commonly
use techniques from KD. Our code is available at
https://github.com/hchoi71/MIX-KD.
