  Knowledge distillation, which involves extracting the "dark knowledge" from a
teacher network to guide the learning of a student network, has emerged as an
essential technique for model compression and transfer learning. Unlike
previous works that focus on the accuracy of student network, here we study a
little-explored but important question, i.e., knowledge distillation
efficiency. Our goal is to achieve a performance comparable to conventional
knowledge distillation with a lower computation cost during training. We show
that the UNcertainty-aware mIXup (UNIX) can serve as a clean yet effective
solution. The uncertainty sampling strategy is used to evaluate the
informativeness of each training sample. Adaptive mixup is applied to uncertain
samples to compact knowledge. We further show that the redundancy of
conventional knowledge distillation lies in the excessive learning of easy
samples. By combining uncertainty and mixup, our approach reduces the
redundancy and makes better use of each query to the teacher network. We
validate our approach on CIFAR100 and ImageNet. Notably, with only 79%
computation cost, we outperform conventional knowledge distillation on CIFAR100
and achieve a comparable result on ImageNet.
