  Model compression is eminently suited for deploying deep learning on
IoT-devices. However, existing model compression techniques rely on access to
the original or some alternate dataset. In this paper, we address the model
compression problem when no real data is available, e.g., when data is private.
To this end, we propose Dream Distillation, a data-independent model
compression framework. Our experiments show that Dream Distillation can achieve
88.5% accuracy on the CIFAR-10 test set without actually training on the
original data!
