## 4 Experimental Results
### Performance Verification Experiment
#### 4.2.4 Comparison Experiment  
To test the impact of different backbone networks on model performance, we tested six backbone networks: ResNet-34, ResNet-50, ResNet-101, VGG16 [45], EfficientNet-B0 [46], and InceptionV4 [47], evaluating their performance after layer-wise feature reconstruction and age label fine-tuning, as shown in Table 6. EfficientNet employs a compound scaling method. By simultaneously adjusting the network's width, depth, and resolution, it balances model accuracy and computational efficiency. The design goal of EfficientNet is to achieve a more efficient utilization of parameters and computational resources, so it generally delivers better performance with lower computational resources compared to the ResNet series architectures. However, when computational resources are abundant, ResNet networks can achieve better performance as the network depth increases. Although VGG16 performs well in traditional classification tasks, age estimation is a regression task rather than a simple classification task. Its relatively simple structure may not be sufficient to capture the complex features needed, and the excessive number of fully connected layers makes the VGG16 network parameter-heavy, resulting in lower computational efficiency and requiring more computational resources during training. Figure 7 visually demonstrates the impact of different backbone networks on the performance of the age estimation task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Parameters** \\ **(Millions)** \\ \end{tabular} } & \multirow{2}{*}{**Layers**} & \multicolumn{4}{c}{**MAE**} \\ \cline{3-6}  & & & **CORAL** & **DLDL** & **MWR** \\ \hline ResNet-34 & 21.8 & 34 & 4.27 & 4.43 & 4.87 \\ ResNet-50 & 25.6 & 50 & 4.12 & 4.28 & 4.75 \\ ResNet-101 & 44.5 & 101 & 3.95 & 4.07 & 4.62 \\ VGG16 [45] & 138.0 & 16 & 4.30 & 4.47 & 4.84 \\ EfficientNet-B0 [46] & 5.3 & 237 & 4.01 & 4.15 & 4.68 \\ InceptionV4 [47] & 42.5 & 48 & 4.10 & 4.22 & 4.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of models in terms of parameters, layers, and MAE under different regression methods.  
Figure 7: Performance comparison of six backbone networks—ResNet-34, ResNet-50, ResNet-101, VGG16, EfficientNet-B0, and InceptionV4—on three age estimation tasks: CORAL, DLDL, and MWR.  
To fully demonstrate the effectiveness of the layer-by-layer distillation approach, we directly trained and fine-tuned three baseline models on the MORPH-2 dataset with multiple occlusion types. The occlusion types in the dataset are shown in Table 7, and we compare the experimental results with those of our student model.  
The results in Table 8 show that the layer-wise feature reconstruction combined with fine-tuning significantly outperforms the baseline model trained directly on the occluded dataset. This is because the model trained directly on the occluded dataset relies solely on visible features, failing to fully exploit the relationship between the occluded and unoccluded regions, which results in suboptimal feature representations. Additionally, the model learns a substantial number of noise features introduced by occlusions, further degrading its performance. In contrast, layer-wise feature reconstruction allows the model to progressively reconstruct features from low-level to high-level, ensuring that it can learn more informative and accurate facial features while gradually mitigating the impact of noise features.