## 4 Experimental Results and Analysis
### Ablation Experiments  
The results of the ablation experiments are summarized in Table 1, which demonstrates the performance improvements achieved by introducing SLPF and LA in all the deep learning models used in our experiments. In general, SLPF led to accuracy improvements ranging from 2.10% to 9.65% for all DCNNs. The average accuracy improvements it brought are 4.78% and 5.21% on CIFAR100 and Tiny-ImageNet, respectively. Furthermore, LA contributed to accuracy improvements ranging from 1.44% to 3.57% for all DCNNs. On average, it led to accuracy improvements of 2.10% and 2.63% on CIFAR100 and Tiny-ImageNet, respectively. In addition, it is observed that both SLPF and LA brought more performance improvement to SqueezeNet, MobileNetv2, and ShuffleNetv2 models than on other models. This is mainly because these three models are lightweight models with fewer parameters, which inherently limits their learning capability. By incorporating SLPF and LA, these models can effectively utilize the features from each layer, thereby strengthening their learning capability. To present how SLPF and LA make effects during the process, we plotted the performance of SqueezeNet against the increasing epoch during training, and the results are shown in Figures 13 and 14. Figure 13 displays that at roughly 60, 120, and 160 epochs, the learning rate drops and the accuracy increases abruptly. If the learning rate remains constant, the loss may oscillate around its minimum. Once the learning rate is reduced, the amplitude of oscillation keeps decreasing until reaches its minimum, i.e., the highest accuracy. However, decreasing the learning rate more frequently will keep the loss at its local minimum rather than reaching the global optimum. Therefore, we choose to decrease the learning rate only at 60, 120, and 160 epochs. It can be seen from Figures 13 and 14 that in the early stages of training, the model has not yet extracted very good features from the deep layers of the convolutional neural network. At this time, the shallow features have a significant impact on the final prediction. Therefore, there is a huge gap in accuracy and loss value between the model with added SLPF and the original model. As the number of training times increases, better features are extracted from deep layers; thus, the gap between the two gradually narrows. However, the shallow layer still contains features that have an impact on the prediction results and also do not exist in the deep layer, so there is still a significant gap. In addition, we can also see that the model with LA added did not \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **The Improvement of** \\  & **Cifar100 Accuracy (\%)** & **Tiny-ImageNet Accuracy (\%)** & **Accuracy Compared to the** \\  & & & **Previous Model on Cifar100** \\  & & & **and Tiny-ImageNet** \\ \hline SqueezeNet & 70.28 & 58.15 & \\ SLPF-SqueezeNet & 75.84 (+5.56) & 63.76 (+5.61) & 7.91\% and 9.65\% \\ LA-SLPF-SqueezeNet & 77.39 (+1.55) & 65.68 (+1.92) & 2.04\% and 3.01\% \\ \hline MobileNetv2 & 68.55 & 56.09 & \\ SLPF-MobileNetv2 & 72.38 (+3.83) & 60.16 (+4.07) & 5.59\% and 7.26\% \\ LA-SLPF-MobileNetv2 & 74.25 (+1.87) & 62.31 (+2.15) & 2.58\% and 3.57\% \\ \hline ShuffleNetv2 & 70.25 & 60.35 & \\ SLPF-ShuffleNetv2 & 74.26 (+4.01) & 62.40 (+2.05) & 5.71\% and 3.40\% \\ LA-SLPF-ShuffleNetv2 & 76.40 (+2.14) & 64.23 (+1.83) & 2.88\% and 2.93\% \\ \hline ResNet18 & 76.18 & 64.29 & \\ SLPF-ResNet18 & 78.15 (+1.97) & 66.12 (+1.83) & 2.59\% and 2.85\% \\ LA-SLPF-ResNet18 & 79.37 (+1.22) & 67.41 (+1.29) & 1.56\% and 1.95\% \\ \hline GoogleNet & 76.77 & 66.34 & \\ SLPF-GoogleNet & 78.38 (+1.61) & 68.27 (+1.93) & 2.10\% and 2.91\% \\ LA-SLPF-GoogleNet & 79.51 (+1.13) & 69.44 (+1.17) & 1.44\% and 1.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of multiple models on CIFAR100 and Tiny-ImageNet.