## 4 Experiments
### Experimental Results and Analysis  
Experimental Results and Analysis of Hypothesis Proofs  
This paper puts forward a hypothesis in Section 2.3: \"using the LLE algorithm as a loss function for model training before pruning plays a positive role in reducing the loss of model performance during pruning\". To prove this hypothesis, corresponding experiments are conducted on the CIFAR-100, BelgiumTS, and GTSRB datasets. Then, the influence of combining the LLE method in model pruning on the loss of model accuracy is analyzed, and the experimental results are shown in Tables 1 and 2. Table 1 compares ASFP [21] with ASFP_LLE methods, PFEC [22] with PFEC_LLE methods, LWCE [23] with LWCE_LLE methods, and FPGM with FPGM_LLE methods as the four groups of methods in the demonstration experiments in this section. In each group of comparison methods, whether to combine the LLE method as the loss function in the training process before pruning is used as the only variable in the experiment, and the other experimental parameters are the same, such as the clipped model, pruning method, data set, pruning ratio, etc. ASFP pruning takes the L2 norm size of the filter as the evaluation standard, and the filter with a small norm value is removed;  
The PFEC pruning method calculates the L1 norm value of the channel and then crops out the smaller channel. The amount of clipping depends on the acceleration ratio;  
The LWCE pruning method reduces the parameters required by the network by pruning unimportant connections (L1/L2 norms) in the trained network. In the experiment, the ASFP_LLE, PFEC_LLE, LWCE_LE, and FPGM_LLE methods are compared with the baseline method, which is realized by combining the LLE method with the corresponding pruning method, i.e., the LLE method is used as the loss function before the model pruning. In this section, the ViT model is cut by using the above pruning method on the CIFAR-100 dataset, and the pruning ratios are 60%, 70%, 80%, 90%, and 95%, respectively. The experimental results are shown in Table 1.  
In Table 1, the 1 st row shows the clipping ratio of the model; the 1 st column shows the pruning method of the clipping model; the 3 rd, 5 th, 7 th, and 9 th rows show the accuracy of the model tested on the dataset after the model is cut with the four baseline pruning methods ASFP, PFEC, LWCE, and FPGM; the 2 nd, 4 th, 6 th, and 8 th rows show the accuracy of the pruning model tested on the dataset after the model is cut by the corresponding comparative pruning method. By analyzing the experimental results in Table 1, it can be found that when cutting the ViT model at 60% to 95% by using the ASFP_LLE pruning method, the accuracy of the model is 0.76%, 1.22%, 1.31%, 2.49%, and 2.48% higher than that of the cutting the model by using the ASFP pruning method, respectively. When cutting the model at 60% to 95% by the PFEC_LLE pruning method, the accuracy of the model test is 0.89%, 1.59%, 1.13%, 2.19%, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pruning Method\textbackslash{}Pruning Rate** & **Baseline** & **60\%** & **70\%** & **80\%** & **90\%** & **95\%** \\ \hline ASFP\_LLE & 67.27 & 64.82 & 63.54 & 60.98 & 45.64 & 37.93 \\ ASFP & 67.27 & 64.06 & 62.32 & 59.67 & 43.15 & 35.45 \\ PFEC\_LLE & 67.27 & 66.26 & 65.43 & 61.56 & 48.46 & 36.98 \\ PFEC & 67.27 & 65.37 & 63.83 & 60.43 & 46.27 & 34.27 \\ LWCE\_LLE & 67.27 & 64.38 & 63.16 & 60.44 & 48.73 & 33.10 \\ LWCE & 67.27 & 63.84 & 61.78 & 59.32 & 46.35 & 30.46 \\ FPGM\_LLE & 67.27 & 65.32 & 63.41 & 60.85 & 43.96 & 33.52 \\ FPGM & 67.27 & 64.63 & 62.25 & 59.41 & 42.25 & 31.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ablation experiments of the ViT model with different pruning methods on the CIFAR-100 dataset.