  We present a novel framework of knowledge distillation that is capable of
learning powerful and efficient student models from ensemble teacher networks.
Our approach addresses the inherent model capacity issue between teacher and
student and aims to maximize benefit from teacher models during distillation by
reducing their capacity gap. Specifically, we employ a neural architecture
search technique to augment useful structures and operations, where the
searched network is appropriate for knowledge distillation towards student
models and free from sacrificing its performance by fixing the network
capacity. We also introduce an oracle knowledge distillation loss to facilitate
model search and distillation using an ensemble-based teacher model, where a
student network is learned to imitate oracle performance of the teacher. We
perform extensive experiments on the image classification datasets---CIFAR-100
and TinyImageNet---using various networks. We also show that searching for a
new student model is effective in both accuracy and memory size and that the
searched models often outperform their teacher models thanks to neural
architecture search with oracle knowledge distillation.
