  Current approaches for Knowledge Distillation (KD) either directly use
training data or sample from the training data distribution. In this paper, we
demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for
image classification networks. For illustration, we consider scenarios where
this is a complete absence of training data, or mismatched stimulus has to be
used for augmenting a small amount of training data. We demonstrate that
stimulus complexity is a key factor for distillation's good performance. Our
examples include use of various datasets for stimulating MNIST and CIFAR
teachers.
