  When deploying pre-trained neural network models in real-world applications,
model consumers often encounter resource-constraint platforms such as mobile
and smart devices. They typically use the pruning technique to reduce the size
and complexity of the model, generating a lighter one with less resource
consumption. Nonetheless, most existing pruning methods are proposed with the
premise that the model after being pruned has a chance to be fine-tuned or even
retrained based on the original training data. This may be unrealistic in
practice, as the data controllers are often reluctant to provide their model
consumers with the original data. In this work, we study the neural network
pruning in the data-free context, aiming to yield lightweight models that are
not only accurate in prediction but also robust against undesired inputs in
open-world deployments. Considering the absence of the fine-tuning and
retraining that can fix the mis-pruned units, we replace the traditional
aggressive one-shot strategy with a conservative one that treats the pruning as
a progressive process. We propose a pruning method based on stochastic
optimization that uses robustness-related metrics to guide the pruning process.
Our method is implemented as a Python program and evaluated with a series of
experiments on diverse neural network models. The experimental results show
that it significantly outperforms existing one-shot data-free pruning
approaches in terms of robustness preservation and accuracy.
