# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 3 Experiments
### Experimental Results  
The performance of the proposed model was evaluated using the CIFAR-10 test dataset. To assess its effectiveness, standard classification metrics are used, including precision, \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Input Size & \(128\times 128\) \\ Initial Learning Rate & 0.00001 \\ Optimizer & Adam \\ Batch Size & 16 \\ Cost Function & MSE + \(\mathcal{L}_{\text{smooth}}\) + \(\mathcal{L}_{\text{detail}}\) + \(\mathcal{L}_{\text{residual}}\) \\ \(\lambda_{1}\) (Smoothness Loss Weight) & 1 \\ \(\lambda_{2}\) (Detail Loss Weight) & 4 \\ \(\lambda_{3}\) (Residual Loss Weight) & 3 \\ Epoch & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter details of the proposed method. recall, F1-score, accuracy, and confusion matrix. Precision and recall were calculated using Equations (10) and (11), where TP denotes true positives, TN denotes true negatives, FP denotes false positives, and FN denotes false negatives. The F1-score was calculated using Equation (12). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{9}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{F1}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{12}\]  
The outputs of the proposed method are used as inputs for the specified deep learning models and compared with the models' performance when using grayscale images as input. Across all models, the proposed method consistently outperforms the grayscale baseline. ResNet50 achieves an accuracy of 86.32% when using the proposed method, compared to 85.45% with grayscale inputs. Similarly, ResNet18 shows an improvement from 85.43% to 86.02%, and MobileNetV2 shows the most pronounced gain, increasing from 83.02% to 85.57%. These results suggest that the proposed method provides a more informative input representation, leading to better classification performance across different architectures, as shown in Table 4.  
Figures 2-4 present comparative analyses of precision, recall, and F1-score. These comparisons illustrate the performance differences between using the output of the proposed method as input to deep learning models and using grayscale images as input to the same models ResNet18, ResNet50, and MobileNetV2. Figure 5 illustrates the confusion matrices for each experimental configuration, highlighting the impact of the proposed method compared to grayscale image inputs across three deep learning architectures: ResNet18, ResNet50, and MobileNetV2. A comparative analysis reveals that the proposed feature extraction approach consistently enhances classification performance. With ResNet18, for example, the number of correctly classified samples increased significantly in classes such as plane (from 861 to 875) and bird (from 768 to 796). Similarly, ResNet50 demonstrated improvements in the classifications of plane (841 to 868), deer (820 to 832), and cat (722 to 741). MobileNetV2 exhibited particularly more gains, with correct predictions increasing in the car (907 to 951), plane (794 to 844), deer (818 to 850), and ship (887 to 930) classes. When aggregated across all classes, the total number of correctly classified samples increased by 67 for ResNet18 (from 8683 to 8750), 69 for ResNet50 (from 8717 to 8786), and 132 for MobileNetV2 (from 8597 to 8729). These results underscore the effectiveness of the proposed representation in providing more discriminative features, thereby enhancing model performance across a variety of architectures and particularly in categories where inter-class confusion is typically higher, such as car and truck or cat and dog.