  To obtain lower inference latency and less memory footprint of deep neural
networks, model quantization has been widely employed in deep model deployment,
by converting the floating points to low-precision integers. However, previous
methods (such as quantization aware training and post training quantization)
require original data for the fine-tuning or calibration of quantized model,
which makes them inapplicable to the cases that original data are not accessed
due to privacy or security. This gives birth to the data-free quantization
method with synthetic data generation. While current data-free quantization
methods still suffer from severe performance degradation when quantizing a
model into lower bit, caused by the low inter-class separability of semantic
features. To this end, we propose a new and effective data-free quantization
method termed ClusterQ, which utilizes the feature distribution alignment for
synthetic data generation. To obtain high inter-class separability of semantic
features, we cluster and align the feature distribution statistics to imitate
the distribution of real data, so that the performance degradation is
alleviated. Moreover, we incorporate the diversity enhancement to solve
class-wise mode collapse. We also employ the exponential moving average to
update the centroid of each cluster for further feature distribution
improvement. Extensive experiments based on different deep models (e.g.,
ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our
proposed ClusterQ model obtains state-of-the-art performance.
