  Several methods of knowledge distillation have been developed for neural
network compression. While they all use the KL divergence loss to align the
soft outputs of the student model more closely with that of the teacher, the
various methods differ in how the intermediate features of the student are
encouraged to match those of the teacher. In this paper, we propose a
simple-to-implement method using auxiliary classifiers at intermediate layers
for matching features, which we refer to as multi-head knowledge distillation
(MHKD). We add loss terms for training the student that measure the
dissimilarity between student and teacher outputs of the auxiliary classifiers.
At the same time, the proposed method also provides a natural way to measure
differences at the intermediate layers even though the dimensions of the
internal teacher and student features may be different. Through several
experiments in image classification on multiple datasets we show that the
proposed method outperforms prior relevant approaches presented in the
literature.
