  Standard Knowledge Distillation (KD) approaches distill the knowledge of a
cumbersome teacher model into the parameters of a student model with a
pre-defined architecture. However, the knowledge of a neural network, which is
represented by the network's output distribution conditioned on its input,
depends not only on its parameters but also on its architecture. Hence, a more
generalized approach for KD is to distill the teacher's knowledge into both the
parameters and architecture of the student. To achieve this, we present a new
Architecture-aware Knowledge Distillation (AKD) approach that finds student
models (pearls for the teacher) that are best for distilling the given teacher
model. In particular, we leverage Neural Architecture Search (NAS), equipped
with our KD-guided reward, to search for the best student architectures for a
given teacher. Experimental results show our proposed AKD consistently
outperforms the conventional NAS plus KD approach, and achieves
state-of-the-art results on the ImageNet classification task under various
latency settings. Furthermore, the best AKD student architecture for the
ImageNet classification task also transfers well to other tasks such as million
level face recognition and ensemble learning.
