🤖 AI Summary
Neural network pruning under data-unavailable scenarios remains challenging, particularly in preserving model robustness without access to original training data.
Method: This paper proposes a robustness-preserving pruning framework that operates without any training data. Departing from mainstream fine-tuning–dependent paradigms, it introduces robustness metrics—such as gradient sensitivity and adversarial response—as explicit supervision signals into the data-free pruning pipeline. The method integrates a progressive, conservative pruning strategy with random-optimization–driven channel-level sparsification to jointly optimize both accuracy and open-world robustness.
Results: Extensive experiments across multiple CNN architectures demonstrate that our approach achieves an average 12.3% improvement in robust accuracy over state-of-the-art data-free pruning methods, while constraining clean accuracy degradation to within 1.5%. This significantly enhances the practicality and reliability of lightweight models deployed on resource-constrained devices.
📝 Abstract
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 a 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 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 model 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 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.
