# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 5 Discussion  
The paper delves into the realm of model compression techniques, specifically exploring the synergies between knowledge distillation and two pruning strategies--weight pruning and channel pruning. These techniques aim to reduce the computational and memory requirements of deep neural networks, enhancing overall model efficiency. The study introduces a novel metric termed \"Performance Efficiency\" to evaluate how these pruning strategies impact both model compression and performance. In Figures 3 and 4, we present the Efficiency comparison for different pruning methods on CIFAR-100 and CIFAR-10. Importantly, the paper reveals that weight pruning is particularly well-suited to knowledge distillation, outperforming channel pruning in this context. The discussion highlights the pivotal role of knowledge distillation (KD) in the combined approach. While both weight and channel pruning contribute significantly to model compression, the paper accentuates that weight pruning, when combined with KD, showcases enhanced efficiency. The adoption of weight pruning proves more advantageous in the knowledge distillation framework, ensuring a substantial reduction in parameters without a commensurate decrease in accuracy. Weight pruning targets finer granularity within the network's architecture by removing individual parameters in neural network layers. This allows for more comprehensive compression without significantly affecting the network's representational capacity. Weight pruning also aligns well with the process of knowledge distillation, effectively leveraging the distilled knowledge from the teacher model to emphasize the most relevant and informative connections in the student model. On the other hand, channel pruning eliminates entire channels of feature maps within convolutional layers while leaving fully connected layers untouched. However, channel pruning can still contribute to efficiency gains in certain scenarios. The paper emphasizes that the choice between weight pruning and channel pruning depends on the specific problem or data characteristics. Weight pruning is recommended when a more precise and comprehensive compression is desired and when knowledge distillation is an important aspect of the model deployment. Channel pruning, on the other hand, may be suitable when a coarser level of compression is acceptable and when preserving all features within the network is not crucial. It is important to note that weight pruning and channel pruning have distinct effects on different layers and components of the network. In Figure 5, we present a comprehensive sparsity comparison between weight pruning and channel pruning techniques applied to three distinct architectures: EfficientNet-b0, EfficientNet-b2, and MobileNet. These architectures were distilled with knowledge from EfficientNet-b1, EfficientNet-b3, and EfficientNet-b3, respectively. Our analysis shows significant differences in sparsity levels between weight pruning and channel pruning, specifically in the final layer of the selected architectures, which corresponds to the last fully connected layer. An important distinction between weight pruning and channel pruning is in the treatment of fully connected layers. Weight pruning applies parameter reduction across all layers, including fully connected layers, ensuring more comprehensive compression. Conversely, channel pruning eliminates entire channels of feature maps within convolutional layers while leaving the fully connected layers untouched. Therefore, researchers should carefully consider the trade-offs between accuracy, compression ratio, and the specific requirements of their problem domain when choosing the appropriate pruning method. For a more in-depth analysis, we conducted a t-test to assess whether weight pruning is a more suitable choice for combining with knowledge distillation (KD). This statistical test helps us understand if weight pruning consistently outperforms channel pruning when integrated with KD.