  Ensembles of deep neural networks have demonstrated superior performance, but
their heavy computational cost hinders applying them for resource-limited
environments. It motivates distilling knowledge from the ensemble teacher into
a smaller student network, and there are two important design choices for this
ensemble distillation: 1) how to construct the student network, and 2) what
data should be shown during training. In this paper, we propose a weight
averaging technique where a student with multiple subnetworks is trained to
absorb the functional diversity of ensemble teachers, but then those
subnetworks are properly averaged for inference, giving a single student
network with no additional inference cost. We also propose a perturbation
strategy that seeks inputs from which the diversities of teachers can be better
transferred to the student. Combining these two, our method significantly
improves upon previous methods on various image classification tasks.
