  Post-training quantization methods use a set of calibration data to compute
quantization ranges for network parameters and activations. The calibration
data usually comes from the training dataset which could be inaccessible due to
sensitivity of the data. In this work, we want to study such a problem: can we
use out-of-domain data to calibrate the trained networks without knowledge of
the original dataset? Specifically, we go beyond the domain of natural images
to include drastically different domains such as X-ray images, satellite images
and ultrasound images. We find cross-domain calibration leads to surprisingly
stable performance of quantized models on 10 tasks in different image domains
with 13 different calibration datasets. We also find that the performance of
quantized models is correlated with the similarity of the Gram matrices between
the source and calibration domains, which can be used as a criterion to choose
calibration set for better performance. We believe our research opens the door
to borrow cross-domain knowledge for network quantization and compression.
