## 4 Experimental Results
### Performance Comparisons of Proposed Method  
The classical DRSN method in the literature and the Log-DRSN method we recommend consist of the same layer and parameters. Based on these findings, we may infer that Log-DRSN is an innovative, effective, and high-performance approach. Figures 8-10 collectively demonstrate the comparative performance of the classical DRSN and the proposed Log-DRSN models under various noise conditions. Figure 8 shows the accuracy ratios of the classical DRSN when exposed to different noise types: Gaussian, Poisson, Speckle, and Salt and Pepper, highlighting the model's varying robustness across noise environments. In contrast, Figure 9 presents the accuracy ratios of the Log-DRSN under the same noise conditions, illustrating its enhanced resilience and consistently higher accuracy compared to the classical approach. Figure 10 offers a direct comparison of both models' accuracy rates across four scenarios, clearly demonstrating the superiority of the Log-DRSN in both noisy and noise-free settings. Although both models share identical architectures and parameter counts, with 336,260 trainable and 1376 non-trainable parameters in the Log-DRSN, the experimental results validate the Log-DRSN as a novel and effective solution for fabric defect classification. Figure 8: Accuracy ratios of the classical DRSN with noise in this study. (**a**) Gaussian noise, (**b**) Poisson noise, (**c**) Speckle noise, and (**d**) Salt and Pepper noise. Figure 7: Labels and sample images in our dataset. Figure 11 illustrates the t-SNE visualizations of feature representations extracted by both the classical DRSN and the proposed Log-DRSN models. The clearer and more distinct clustering observed in the Log-DRSN visualization indicates its superior capability in learning discriminative features for fabric defect classification. When the accuracy rates of the models using both activation functions are examined in Table 3, it is clear that the Log-Leaky ReLU outperforms the traditional Leaky ReLU in terms of accuracy. Despite the increased computational complexity reflected in the computational times reported in Table 4, the Log-Leaky ReLU activation function, as implemented in the Log-DRSN model, was preferred in this study because achieving higher accuracy was prioritized over minimizing computation time. When examining Figure 12, it is evident that Log-DRSN starts learning faster than the classical DRSN. This figure is based on the comparison of accuracy rates at three sample iteration points. In the fifth iteration, the classical DRSN reached 63%, while Log-DRSN  
Figure 11: t-SNE images obtained by executing the classical DRSN and the Log-DRSN. (**a**) Training samples and (**b**) samples with Log-DRSN. Figure 10: Accuracy rates of the classical DRSN and Log-DRSN. (**a**) Classical DRSN from all noise, (**b**) Log-DRSN from all noise, (**c**) Classical DRSN without noise, and (**d**) Log-DRSN without noise. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Classical DRSN**} & \multicolumn{6}{c}{**Log-DRSN**} \\ \cline{2-11}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline
**Speckle** & 0.8280 & 0.8522 & 0.7952 & 0.8227 & 0.9706 & 0.8593 & 0.8761 & 0.8450 & 0.8602 & 0.9794 \\ \hline
**Salt and Pepper** & 0.8561 & 0.8800 & 0.8289 & 0.8536 & 0.9740 & 0.9207 & 0.9303 & 0.9110 & 0.9205 & 0.9912 \\ \hline
**Gaussian** & 0.9479 & 0.9539 & 0.9860 & 0.9696 & 0.9966 & 0.9603 & 0.9628 & 0.9562 & 0.9594 & 0.9978 \\ \hline
**Poisson** & 0.9548 & 0.9599 & 0.9830 & 0.9713 & 0.9969 & 0.9640 & 0.9670 & 0.9603 & 0.9413 & 0.9975 \\ \hline
**Without noise** & 0.9686 & 0.9717 & 0.9654 & 0.9684 & 0.9985 & **0.9917** & 0.9921 & 0.9908 & 0.9914 & 0.9997 \\ \hline \end{tabular} Note: Bold values represent the highest accuracy scores.  
\end{table} 
Table 3: Performance metrics of the classical DRSN and Log-DRSN. achieved 87%.