  Zero-shot quantization is a promising approach for developing lightweight
deep neural networks when data is inaccessible owing to various reasons,
including cost and issues related to privacy. By exploiting the learned
parameters ($\mu$ and $\sigma$) of batch normalization layers in an
FP32-pre-trained model, zero-shot quantization schemes focus on generating
synthetic data. Subsequently, they distill knowledge from the pre-trained model
(teacher) to the quantized model (student) such that the quantized model can be
optimized with the synthetic dataset. However, thus far, zero-shot quantization
has primarily been discussed in the context of quantization-aware training
methods, which require task-specific losses and long-term optimization as much
as retraining. We thus introduce a post-training quantization scheme for
zero-shot quantization that produces high-quality quantized networks within a
few hours. Furthermore, we propose a framework called Genie~that generates data
suited for quantization. With the data synthesized by Genie, we can produce
robust quantized models without real datasets, which is comparable to few-shot
quantization. We also propose a post-training quantization algorithm to enhance
the performance of quantized models. By combining them, we can bridge the gap
between zero-shot and few-shot quantization while significantly improving the
quantization performance compared to that of existing approaches. In other
words, we can obtain a unique state-of-the-art zero-shot quantization approach.
The code is available at \url{https://github.com/SamsungLabs/Genie}.
