# 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.  
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our understanding of the full spectrum of techniques available for reducing model size and computational complexity. Neglecting to explore channel pruning could result in missed opportunities for optimizing model performance, particularly in scenarios where computational resources are limited or where deployment on resource-constrained devices is necessary. By comprehensively examining both weight pruning and channel pruning in the context of knowledge distillation, we gain valuable insights into their comparative effectiveness and applicability across different architectures. This knowledge empowers researchers and practitioners to make informed decisions when selecting pruning techniques for specific use cases, ultimately facilitating the development of leaner, more efficient deep learning models that can be deployed in real-world applications. Our primary objective is fourfold:  
* We begin with a comprehensive comparison of two renowned pruning methods, namely weight pruning and channel pruning, in combination with knowledge distillation (KD). Both methods utilize the L1 norm as the shared criteria, allowing for a fair and rigorous evaluation of their efficacy in compressing models while preserving performance.
* Furthermore, we delve into the concept of \"Performance Efficiency\" in our framework, a novel formula designed to quantify model efficiency by considering reductions in parameters alongside accuracy. This metric serves as a valuable tool for assessing the effectiveness of compression techniques in real-world deployment scenarios.
* Additionally, we employ rigorous statistical analysis, including t-tests, to evaluate the significance of differences between pruning methods in terms of their impact on model performance. By subjecting our findings to statistical scrutiny, we ensure the reliability and robustness of our conclusions, enhancing the credibility of our research outcomes.
* To demonstrate the effectiveness of our proposed pipeline, we conducted evaluations involving 10 model combinations on the CIFAR-10 and CIFAR-100 datasets. These experiments provide empirical evidence of the advantages and limitations of each approach, shedding light on their applicability in practical settings.  
The rest of the paper is organized as follows: The \"Related Work\" section provides an overview of the state-of-the-art; the \"Materials and Methods\" section describes our experimental setup and approach; the \"Experiments\" section presents detailed empirical findings and their broader implications; and finally, the \"Discussion\" and \"Conclusion\" sections analyze and summarize our findings, offering additional insights into this task.