## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis
#### 4.3.3 Model Ablation Experiments  
(1) Squeezing operator  
In the SE module section, this study compares the effects of two pooling strategies, max-pooling and average-pooling, used in the squeezing operation, with the specific results presented in Table 9.  
From Table 9, we can see that both pooling methods demonstrate exemplary performance. However, average pooling performs slightly better, which may be due to its greater robustness in handling anomalous or outlier data compared to maximum pooling. It can also better capture the statistical characteristics and patterns of the data. Additionally, this experiment verifies the high robustness of the SE block, as the impact of incorporating the SE block on the performance of the final model remains relatively stable and high, with no significant difference, regardless of the pooling method used. (2) Different activation functions  
The activation functions play a crucial role in neural networks. Their primary function is to introduce nonlinear factors, allowing neural networks to learn and model complex data patterns. Regardless of its depth, such a network would be severely limited in its expressive power, capable only of approximating linear functions or linear combinations, and unable to capture the intricate nonlinear relationships within the data. Therefore, this study investigates the effect of the activation function used for the excitation operator in SE-VGG16 on the results presented in Table 10. The experimental outcomes in Table 10 indicate that employing the ReLU activation function yields greater accuracy, making SE-VGG16 more effective. (3) VGG16 integrates SE blocks at different stages  
We integrated SE blocks into each stage of VGG16 sequentially to investigate their impact at different levels of the network. As illustrated in Table 11, integrating SE blocks at different stages within the VGG16 architecture leads to varying degrees of performance enhancement across the model. This indicates that the benefits produced by SE blocks at various stages are complementary and can collectively enhance the network's overall performance. Additionally, it is essential to note that while the number of model parameters increases with the addition of SE blocks, this growth remains relatively limited and does not significantly increase model training costs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Functions** & **VGG16** & **SE-VGG16** \\ \hline ReLU & **89.82\%** & **94.12\%** \\ tanh & 88.64\% & 92.25\% \\ sigmoid & 88.98\% & 92.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of using different nonlinear activation functions for the excitation operator in SE-VGG16 (%). \begin{table}
\begin{tabular}{c c} \hline \hline
**Stage** & **F1** \\ \hline VGG-16 & 89.82\% & 141.3 M \\ SE\_Stage\_2 & 93.21\% & 141.3 M \\ SE\_Stage\_3 & 93.33\% & 141.5 M \\ SE\_Stage\_4 & 93.35\% & 143.1 M \\ SE\_All & **94.12\%** & **145.3 M** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F1 score (%) and number of parameters of integrating SE with VGG16 at different stages on gesture recognition. \begin{table}
\begin{tabular}{c c} \hline \hline
**Squeeze** & **F1** \\ \hline Max pooling & 93.18\% \\ Avg pooling & **94.12\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: F1 score of using different squeezing operators in SE-VGG16 (%). (4) Impact of SE: VGG16-SE, NoSqueeze, VGG16  
To verify the performance of the SE module, we first remove the pooling layer. The original fully connected (FC) layer is replaced with a 1 \(\times\) 1 convolutional layer. We refer to the structure after the replacement as NoSqueeze, and the experimental results are shown in Table 12.  
From the detailed data in Table 12, we can see that the F1 score of the model improves by 4.13% when the SE module is incorporated into the VGG16 network architecture. This improvement is reflected in the increased recognition accuracy and the enhanced learning ability of the model for complex features.