  Deep Neural Networks (DNNs) have gained considerable attention in the past
decades due to their astounding performance in different applications, such as
natural language modeling, self-driving assistance, and source code
understanding. With rapid exploration, more and more complex DNN architectures
have been proposed along with huge pre-trained model parameters. The common way
to use such DNN models in user-friendly devices (e.g., mobile phones) is to
perform model compression before deployment. However, recent research has
demonstrated that model compression, e.g., model quantization, yields accuracy
degradation as well as outputs disagreements when tested on unseen data. Since
the unseen data always include distribution shifts and often appear in the
wild, the quality and reliability of quantized models are not ensured. In this
paper, we conduct a comprehensive study to characterize and help users
understand the behaviors of quantized models. Our study considers 4 datasets
spanning from image to text, 8 DNN architectures including feed-forward neural
networks and recurrent neural networks, and 42 shifted sets with both synthetic
and natural distribution shifts. The results reveal that 1) data with
distribution shifts happen more disagreements than without. 2)
Quantization-aware training can produce more stable models than standard,
adversarial, and Mixup training. 3) Disagreements often have closer top-1 and
top-2 output probabilities, and $Margin$ is a better indicator than the other
uncertainty metrics to distinguish disagreements. 4) Retraining with
disagreements has limited efficiency in removing disagreements. We opensource
our code and models as a new benchmark for further studying the quantized
models.
