  Convolutional neural networks are able to learn realistic image priors from
numerous training samples in low-level image generation and restoration. We
show that, for high-level image recognition tasks, we can further reconstruct
"realistic" images of each category by leveraging intrinsic Batch Normalization
(BN) statistics without any training data. Inspired by the popular VAE/GAN
methods, we regard the zero-shot optimization process of synthetic images as
generative modeling to match the distribution of BN statistics. The generated
images serve as a calibration set for the following zero-shot network
quantizations. Our method meets the needs for quantizing models based on
sensitive information, \textit{e.g.,} due to privacy concerns, no data is
available. Extensive experiments on benchmark datasets show that, with the help
of generated data, our approach consistently outperforms existing data-free
quantization methods.
