  Despite the success of Deep Learning (DL), the deployment of modern DL models
requiring large computational power poses a significant problem for
resource-constrained systems. This necessitates building compact networks that
reduce computations while preserving performance. Traditional Knowledge
Distillation (KD) methods that transfer knowledge from teacher to student (a)
use a single-stage and (b) require the whole data set while distilling the
knowledge to the student. In this work, we propose a new method called
Stagewise Knowledge Distillation (SKD) which builds on traditional KD methods
by progressive stagewise training to leverage the knowledge gained from the
teacher, resulting in data-efficient distillation process. We evaluate our
method on classification and semantic segmentation tasks. We show, across the
tested tasks, significant performance gains even with a fraction of the data
used in distillation, without compromising on the metric. We also compare our
method with existing KD techniques and show that SKD outperforms them.
Moreover, our method can be viewed as a generalized model compression technique
that complements other model compression methods such as quantization or
pruning.
