  Knowledge distillation aims to transfer useful information from a teacher
network to a student network, with the primary goal of improving the student's
performance for the task at hand. Over the years, there has a been a deluge of
novel techniques and use cases of knowledge distillation. Yet, despite the
various improvements, there seems to be a glaring gap in the community's
fundamental understanding of the process. Specifically, what is the knowledge
that gets distilled in knowledge distillation? In other words, in what ways
does the student become similar to the teacher? Does it start to localize
objects in the same way? Does it get fooled by the same adversarial samples?
Does its data invariance properties become similar? Our work presents a
comprehensive study to try to answer these questions. We show that existing
methods can indeed indirectly distill these properties beyond improving task
performance. We further study why knowledge distillation might work this way,
and show that our findings have practical implications as well.
