  We propose a novel method for training a conditional generative adversarial
network (CGAN) without the use of training data, called zero-shot learning of a
CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a
pre-trained discriminative (classification) model and does not need any
training data. In particular, the conditional generator is trained to produce
labeled synthetic samples whose characteristics mimic the original training
data by using the statistics stored in the batch normalization layers of the
pre-trained model. We show the usefulness of ZS-CGAN in data-free quantization
of deep neural networks. We achieved the state-of-the-art data-free network
quantization of the ResNet and MobileNet classification models trained on the
ImageNet dataset. Data-free quantization using ZS-CGAN showed a minimal loss in
accuracy compared to that obtained by conventional data-dependent quantization.
