Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression

Abstract

In recent years, model compression techniques have gained significant attention as a means to reduce the computational and memory requirements of deep neural networks. Knowledge distillation and pruning are two prominent approaches in this domain, each offering unique advantages in achieving model efficiency. This paper investigates the combined effects of knowledge distillation and two pruning strategies, weight pruning and channel pruning, on enhancing compression efficiency and model performance. The study introduces a metric called “Performance Efficiency” to evaluate the impact of these pruning strategies on model compression and performance. Our research is conducted on the popular datasets CIFAR-10 and CIFAR-100. We compared diverse model architectures, including ResNet, DenseNet, EfficientNet, and MobileNet. The results emphasize the efficacy of both weight and channel pruning in achieving model compression. However, a significant distinction emerges, with weight pruning showing superior performance across all four architecture types. We realized that the weight pruning method better adapts to knowledge distillation than channel pruning. Pruned models show a significant reduction in parameters without a significant reduction in accuracy.

Keywords

• knowledge distillation
• network efficiency
• parameter reduction
• unstructured pruning
• structured pruning