# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 4 Experiment
### Ablation and Baseline Comparison Experiments
#### 4.4.3 Comparison Between the Proposed Migration Mechanism and Mainstream Model Adaptation Methods  
Mainstream model adaptation methods mainly include strategies like model pruning, knowledge distillation, and lightweight structural design. To verify the effectiveness of the proposed transfer feature extraction mechanism, two representative baseline methods were selected for comparison: structured pruning and soft label distillation. The inference performance of these methods was evaluated against the proposed method on the edge model.  
In the pruning part, a structured channel pruning strategy was adopted. Specifically, pruning operations were performed on the conv1 and conv2 layers within the head.classifier module of the edge model to reduce redundant network parameters and improve the model's inference efficiency on edge devices.  
In the distillation part, the classical soft label distillation method was adopted. This strategy fixes the teacher model (the pre-trained edge model weights) and compares the logits bias outputs of the student model under the same input, achieving parameter-level consistency learning. By aligning the outputs of head.score.bias[class_id] between the teacher and student models and minimizing the Mean Squared Error (MSE), local parameter knowledge transfer and learning for the target category are achieved.  
The inference results are shown in Table 7.  
As shown in the table, compared with the original model, the precision, recall, and AP metrics for each category exhibit slight fluctuations after applying the pruning strategy. However, the overall differences are not significant, indicating that the model still maintains strong representation capability even with reduced parameter volume. In addition, the soft label distillation strategy demonstrates performance that is almost identical to, or slightly better than, the original model across all categories. This indicates that effective knowledge transfer can be achieved by aligning and optimizing head.score.bias without modifying the model's structure. In summary, the proposed transfer feature extraction mechanism maintains good robustness and stability compared with mainstream adaptation strategies, such as structured pruning and soft label distillation, further validating its feasibility and practical value in edge scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Model Used** & **Precision** & **Recall** & **AP** \\ \hline A321 & Original Model & 70.30\% & 60.68\% & 53.66\% \\  & Pruning & 67.44\% & 55.77\% & 48.43\% \\  & Distillation & 70.28\% & 60.68\% & 53.71\% \\ A330 & Original Model & 32.26\% & 55.56\% & 52.38\% \\  & Pruning & 33.33\% & 50.00\% & 42.71\% \\  & Distillation & 32.22\% & 55.56\% & 52.38\% \\ Other-plane & Original Model & 44.12\% & 57.69\% & 37.85\% \\  & Pruning & 43.70\% & 56.73\% & 37.61\% \\  & Distillation & 44.11\% & 57.69\% & 37.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Inference results of different model adaptation methods.