  Recently, an extensive amount of research has been focused on compressing and
accelerating Deep Neural Networks (DNN). So far, high compression rate
algorithms require part of the training dataset for a low precision
calibration, or a fine-tuning process. However, this requirement is
unacceptable when the data is unavailable or contains sensitive information, as
in medical and biometric use-cases. We present three methods for generating
synthetic samples from trained models. Then, we demonstrate how these samples
can be used to calibrate and fine-tune quantized models without using any real
data in the process. Our best performing method has a negligible accuracy
degradation compared to the original training set. This method, which leverages
intrinsic batch normalization layers' statistics of the trained model, can be
used to evaluate data similarity. Our approach opens a path towards genuine
data-free model compression, alleviating the need for training data during
model deployment.
