  Despite the fact that deep neural networks are powerful models and achieve
appealing results on many tasks, they are too large to be deployed on edge
devices like smartphones or embedded sensor nodes. There have been efforts to
compress these networks, and a popular method is knowledge distillation, where
a large (teacher) pre-trained network is used to train a smaller (student)
network. However, in this paper, we show that the student network performance
degrades when the gap between student and teacher is large. Given a fixed
student network, one cannot employ an arbitrarily large teacher, or in other
words, a teacher can effectively transfer its knowledge to students up to a
certain size, not smaller. To alleviate this shortcoming, we introduce
multi-step knowledge distillation, which employs an intermediate-sized network
(teacher assistant) to bridge the gap between the student and the teacher.
Moreover, we study the effect of teacher assistant size and extend the
framework to multi-step distillation. Theoretical analysis and extensive
experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet
architectures substantiate the effectiveness of our proposed approach.
