# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 4 Experiments
### Pruning  
In our paper [1], we provide a comprehensive performance comparison of various distillation approaches using 11 network combinations where teacher and student models have either similar or completely different architectures. We compare nine different knowledge distillation methods to determine which one yields the best performance. Our results show that SimKD consistently outperforms all competitors on CIFAR-100 and CIFAR-10. For more detailed analysis and results, please refer to the paper [1]. With our distilled student models identified through the SimKD [11], we performed a series of network pruning experiments on distilled student models. The pruning process consisted of gradually decreasing the number of parameters from 0% to 90% in steps of 10%. To ensure accuracy and reliability, we followed a rigorous approach. We conducted 10 iterations for each teacher-student pair and averaged the results to increase the robustness of our analysis. This repeated averaging helped us reduce potential biases or instabilities in our findings. It considered factors such as the capacity of the initial model, the effect of random initialization, and the need for a comprehensive evaluation of the knowledge transfer process. By averaging the results over multiple iterations, we obtained a more representative understanding of the flexibility and adaptability of the model. These features are very important in evaluating the effectiveness of the knowledge transfer mechanism. After conducting accurate pruning iterations, we focused on accurately calculating and documenting the remaining parameters in both the pruned teacher and student networks. This involved counting the non-zero weights or connections that were retained in the pruned networks. This quantification helped us understand the level of parameter reduction achieved through pruning, providing valuable insights into the streamlined architecture of the model. However, a \"breakpoint\" arises where accuracy drops significantly faster relative to parameter reduction. Remarkably, in our results, many student models achieve comparable or better accuracy than distilled models at this breakpoint. This emphasizes the effectiveness of combining knowledge distillation and pruning for improved efficiency without accuracy loss. At the breakpoint, students leverage the teacher's insights, maintaining performance despite fewer parameters [1]. As shown in Figures 1 and 2, we present a comprehensive pruning analysis for weight and channel pruning on the CIFAR-10 and CIFAR-100 datasets. Through these figures, we explore the relationship between accuracy and the remaining parameters after pruning. Across all model architectures, we consistently observe that weight pruning achieves comparable or higher accuracy at a significantly lower number of remaining parameters, as demonstrated by the corresponding figures for ResNet 101 to 18 and EfficientNet B1 to B0. This trend suggests that weight pruning is adept at identifying and retaining essential parameters while effectively reducing the model's complexity. Consequently, weight  
In contrast, channel pruning exhibits a different pattern, as highlighted in Figures 1 and 2. Although the breakpoint is generally reached with comparable accuracy, the corresponding number of parameters is notably higher in channel pruning. This implies that while channel pruning maintains similar accuracy levels, it retains a larger proportion of parameters compared to weight pruning. Consequently, channel pruning may be considered a more conservative approach in terms of parameter reduction, potentially sacrificing some efficiency gains to maintain greater model capacity. A comparison between weight pruning and channel pruning highlights the inherent trade-offs between model complexity, accuracy, and efficiency. Weight pruning shows its power in significantly reducing the number of parameters while maintaining or even increasing accuracy.