## 2 Method
### JS_Divergence Values  
The similarity filter pruning method based on local linear embedding (S)-LLE) first measures the similarity of filters in each layer of the network according to a criterion, identifying pairs of filters with similar functions to be pruned. Then, one filter in each similar filter pair is removed. In traditional neural network pruning processes, the ADMM framework is combined with filter pruning methods based on the L2 norm. However, some filters with small L2 norm values still hold importance. The similarity filter pruning method can remove filters with similar functions, directly reducing the computational and parameter load of the network model while minimizing accuracy loss through model compression. The SJ-LLE method uses the Jensen-Shannon (JS) divergence value among the filters as the criterion for judging similarity, calculating the JS_divergence value between each filter and others in each layer of the network. Filters with divergence values less than the pruning threshold are identified as similar filter pairs to be pruned. Subsequently, the method calculates the information entropy value of the filters and removes the filter with the smaller information entropy value in the similar filter pair.  
The SJ-LLE method uses Equation (19) to calculate the JS_divergence value between each filter and other filters in each layer of the network [18].  
\[JS(P||Q)=\frac{1}{2}KL(P||\frac{P+Q}{2})+\frac{1}{2}KL(Q||\frac{P+Q}{2}) \tag{19}\]  
During the pruning process, the weights of the model are substituted into the JS_divergence Formula (19) to obtain Formula (20). Meanwhile, the weights are arranged according to their positions, the weight distributions of the two filters are compared for similarity, and the filter with a JS_divergence value less than the pruning threshold is used as a similar filter pair to be pruned.  
\[JS(P||Q)=\frac{1}{2}\sum_{i=1}^{N}p(x_{i})\log\frac{p(x_{i})+q(x_{i})}{2}+ \frac{1}{2}\sum_{i=1}^{N}q(x_{i})\log\frac{p(x_{i})+q(x_{i})}{2} \tag{20}\]  
P and Q, respectively, represent the proportion of weights in the corresponding positions of the filter, and the weight distribution of the two filters is compared based on the JS_divergence value and information entropy value in the comparison process.  
To retain the filter with a richer amount of information in a similar filter pair, the information entropy value of the filter is taken as the evaluation criterion: the larger the information entropy value, the greater the amount of information involved. Thus, the information entropy of the filter is calculated, the filter with a larger information entropy value in the similar filter pair is retained, and the filter with a smaller information entropy value in the similar filter is removed. In this way, the weight element of the filter is zeroed, and the calculation formula of the information entropy [18] is shown in Formula (21).  
\[H(P)=\sum_{i=1}^{N}p(x_{i})\log\biggl{(}\frac{1}{p(x_{i})}\biggr{)} \tag{21}\]The specific steps of the pruning algorithm are as follows (Algorithm 2):  
```
Input: \(N\): filters to be pruned layer by layer. \(\beta_{l}\): pruning threshold. 1: Obtain the absolute value of the weight parameter of \(N\) filters in layer \(i\) in the pre-trained model. 2: Calculate the JS\({}_{-}\) divergence value between \(N\) filters in step 1 and between each filter and other filters, and sort these filters according to the JS\({}_{-}\) divergence value. 3: Select the pre-\(N\beta_{l}\) pair filter sorted in step 2 as the filter pair to be pruned. 4: Calculate the information entropy value of each filter, remove the filter with a small information entropy value from the filters to be pruned in step 3, and retain the filter with a larger information entropy value.
```  
**Algorithm 2**Similar filter pruning algorithm based on JS\({}_{-}\) divergence