Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation
This addresses a critical issue for privacy-sensitive domains like healthcare or finance, offering an incremental improvement over existing pruning methods.
The paper tackles the problem of accuracy degradation in pruned deep neural networks when original training data is unavailable due to privacy concerns, by proposing a data-free knowledge distillation method using synthetic images, which recovers significant accuracy without real data.
Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In privacy-sensitive domains such as healthcare or finance, access to the original training data is often restricted post-deployment due to regulations (e.g., GDPR, HIPAA). This paper proposes a Data-Free Knowledge Distillation framework to bridge the gap between model compression and data privacy. We utilize DeepInversion to synthesize privacy-preserving ``dream'' images from the pre-trained teacher model by inverting Batch Normalization (BN) statistics. These synthetic images serve as a transfer set to distill knowledge from the original teacher to the pruned student network. Experimental results on CIFAR-10 across various architectures (ResNet, MobileNet, VGG) demonstrate that our method significantly recovers accuracy lost during pruning without accessing a single real data point.