# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 4 Experiments
### Performance Evaluation
Table 4 shows a comparison of the classification accuracy of each method under the pair-flipping noise rate of 40% on the dataset Cifar-10. Note that we chose this rate to simulate a moderate to large level of noise that is likely to occur in real-world scenarios. Actually, a similar trend can be obtained for a higher pair-flipping noise rate. As shown in Table 4, our CoNet-MS achieves the highest test accuracy of 88.43% under 40% pair-flipping noise on CIFAR-10, outperforming L2B (87.96%) and co-teaching (73.85%). Moreover, the following conclusions can be drawn:  
* Unlike L2B, which relies solely on meta-weights, CoNet-MS further reduces confirmation bias by cross-validating pseudo-labels between two networks, leading to a 0.47% accuracy gain over L2B. Figure 3: A visualization of the confusion matrix learned by our CoNet-MS model trained with 50% uniform noise. * Compared to co-teaching (73.85%), our method shows stronger resilience to high noise ratios, indicating that meta-learning enhances the precision of the sample selection of co-teaching. In terms of the performance metrics accuracy and Macro F1, Tables 5 and 6 give a comparison of the classification performance of each method under the uniform noise and pair-flipping noise at the rate of 40% on the dataset ISIC2019. Our algorithm shows the best performance. In detail, the following conclusions can be drawn:  
* First of all, under the imbalanced dataset, CoNet-MS outperforms all baseline methods in both noise types. Specifically, under uniform noise, CoNet-MS achieves the highest accuracy of 74.23%, which is a 1.82% improvement compared to L2B. Meanwhile, with a pair-flipping noise rate of 40%, our scheme still achieves the best performance, i.e., 69.75% accuracy, but improves by only 0.51% relative to L2B, reflecting the particular challenges caused by the noise between similar classes in an imbalanced dataset, which should be thoroughly investigated in future work. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Macro F1** \\ \hline Conventional & 62.90 & 0.5918 \\ Cross Entropy & 67.49 & 0.6375 \\ \hline L2B [11] & 69.24 & 0.6658 \\ \hline
**Ours (CoNet-MS)** & **69.75** & **0.6734** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of all schemes under the pair-flipping noise with a rate of 40% on the dataset ISIC2019 (imbalanced dataset). \begin{table}
\begin{tabular}{c c} \hline \hline
**Noise Ratio (\%)** & **40** \\ \hline Conventional & 73.24 \\ Cross Entropy & 73.85 \\ \hline MetaLabelNet [10] & 84.66 \\ \hline L2B [11] & 87.96 \\ \hline
**Ours (CoNet-MS)** & **88.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of test accuracy (%) with 40% pair-flipping noise on CIFAR-10 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Macro F1** \\ \hline Conventional & 70.47 & 0.6852 \\ Cross Entropy & 73.43 & 0.7123 \\ \hline L2B [11] & 72.41 & 0.7098 \\ \hline
**Ours (CoNet-MS)** & **74.23** & **0.7234** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of all schemes under the uniform noise with a rate of 40% on the dataset ISIC2019 (imbalanced dataset). Secondly, compared with results in Tables 3 and 4, that is, the test accuracy of uniform and pair-flipping noises on the balanced dataset, i.e., CIFAR-10, the results in Tables 5 and 6 are generally smaller than those in Tables 3 and 4. It is well known that an imbalanced training dataset will have a negative effect on classification accuracy, even in a training dataset with fully clean labels. * As shown in Table 5, under uniform noise, our method achieves the Macro F1 of 0.7234, surpassing co-teaching and L2B. This indicates that CoNet-MS not only improves overall accuracy but also better balances precision and recall across imbalanced classes. For pair-flipping noise (Table 6), CoNet-MS attains the Macro F1 of 0.6734, marginally exceeding L2B. The smaller Macro F1 gap (0.0076) compared to the uniform noise scenario (0.0136) aligns with the observed accuracy trend.