  Modern convolutional neural networks (CNNs) are known to be overconfident in
terms of their calibration on unseen input data. That is to say, they are more
confident than they are accurate. This is undesirable if the probabilities
predicted are to be used for downstream decision making. When considering
accuracy, CNNs are also surprisingly robust to compression techniques, such as
quantization, which aim to reduce computational and memory costs. We show that
this robustness can be partially explained by the calibration behavior of
modern CNNs, and may be improved with overconfidence. This is due to an
intuitive result: low confidence predictions are more likely to change
post-quantization, whilst being less accurate. High confidence predictions will
be more accurate, but more difficult to change. Thus, a minimal drop in
post-quantization accuracy is incurred. This presents a potential conflict in
neural network design: worse calibration from overconfidence may lead to better
robustness to quantization. We perform experiments applying post-training
quantization to a variety of CNNs, on the CIFAR-100 and ImageNet datasets.
