## 4 Experiments and Result Analysis
### Detail Experiments  
In the proposed DBS-NET, the contribution of each branch to the total loss is controlled by the weight coefficients \(\alpha\) and \(\beta\). To accurately assess the impact of these coefficients on the network, we used a unified backbone (ResNet18) for both branches. To optimize the weight distribution in the Dual-Branch model, we implemented an auto-exploration strategy. In this strategy, the backbone was pre-trained using ResNet18, and trained with the Adam optimizer (learning rate = 0.0003). The data augmentation methods included RandomResizedCrop, GaussianBlur, RandomApply ColorJitter, and RandomGrayscale. The auto-exploration strategy traversed all \(\beta\) in the range [0.1, 0.9], with a step size of 0.1. The \(\alpha\) corresponding to each \(\beta\) is set to \(1-\beta\). Each combination of (\(\alpha\), \(\beta\)) was tested to observe its impact on the performance of the model. The results are listed in Table 7.  
As shown in Table 7, the classification performance of the model is improves with the increase of the supervised branch weight coefficient (\(\alpha\)). When \(\alpha=0.9\) (\(\beta=0.1\)), the highest accuracy (97.66%) is achieved. This indicates that the supervised branch plays a pivotal role in the classification task. It has strong discriminability and directly contributes to the optimization of the classification objective. However, when only the supervised branch is used (i.e., training with only supervised learning), the performance is slightly lower \begin{table}
\begin{tabular}{l l r r r r} \hline
**Model** & **Ablation Setting** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & Backbone & 96.78 & 96.78 & 96.26 & 96.20 \\
2 & Backbone + iDAFF & 97.04 & 97.03 & 96.59 & 96.79 \\
3 & Backbone + Linear Residual & 96.89 & 96.88 & 96.38 & 96.32 \\
4 & Backbone + Weighted Loss & 96.78 & 96.59 & 96.27 & 96.42 \\
5 & Backbone + iDAFF + Linear Residual & 97.42 & 97.21 & 96.84 & 96.97 \\
6 & Backbone + Linear Residual + Weighted Loss & 97.07 & 96.90 & 96.72 & 96.80 \\
7 & Backbone + iDAFF + Weighted Loss & 97.33 & 97.22 & 96.99 & 97.09 \\
8 & Backbone + iDAFF + Linear Residual + Weighted Loss & 97.54 & 97.47 & 97.23 & 97.22 \\ \hline
9 & Backbone + iDAFF + Linear Residual + Weighted Loss \(\mid\) without SSCL branch & 96.67 & 96.31 & 96.57 & 96.43 \\ \hline \end{tabular}
\end{table} 
Table 6: Effectiveness of different modules in a Dual-Branch Network. Figure 8: Effect of weighted loss on model performance. than that of the optimal combination. This suggests that although not directly aligned with the classification objective, the unsupervised branch enhances overall performance by improving the robustness of the feature representations. To further explore the optimal balance between \(\alpha\) and \(\beta\), more extreme values of \(\beta\). Specifically, \(\alpha\) varied in the range [0.92, 0.98] with a step size of 0.02 (meaning \(\beta\) in [0.08, 0.02]). As shown in Figure 9, the performance of the model improves as the supervised branch weight increases from 0.1 to 0.9. When the value of \(\alpha\) exceeds 0.92, the classification accuracy begins to decline. This suggests that the unsupervised branch, as an auxiliary component, effectively complements the supervised branch by balancing discriminative and robust features to achieve higher accuracy. Figure 10 illustrates how varying the weight \(\alpha\) of the unsupervised branch influences classification accuracy. As \(\alpha\) increases from 0.02 to 0.10, accuracy rises steadily, reaching a peak of about 97.6% when \(\alpha\) is in the 0.10-0.20 range. Beyond \(\alpha\approx 0.30\), accuracy begins to drop: the model increasingly emphasises generic instance discrimination at the expense of label-driven separation. When it is pushed to 0.90, accuracy falls to about 95.5%, confirming that excessive reliance on the unsupervised objective reduces class-specific performance.