# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 4 Experiments
### Performance Evaluation  
Comparison with the State-of-the-Art Methods  
Table 3 shows a comparison of the classification accuracy of each scheme under different uniform noise rates on the dataset CIFAR-10. Our algorithm shows the best performance under all noise rate conditions. According to the experimental results in Table 3, the following conclusions can be drawn:  
* Firstly, CoNet-MS shows the best classification performance under many different noise rates among all schemes, which fully demonstrates the superiority and robustness of our proposed scheme. * L2B achieves the second-best test accuracy under all noise scenarios by adjusting the loss weights by meta-learning to counteract noisy labels. However, it is not sufficient to reduce the influence of noisy samples in the loss function, and there is a gap between the pseudo-label assigned to the samples and the ground-truth label. Our scheme makes the following improvement: introducing a dual-network sample filtering mechanism to discard samples with lower confidence and retain more valuable samples at each update. Empirically, compared to L2B, our scheme CoNet-MS achieves better classification accuracy at different noise rates, with a 3% improvement in test accuracy under the scenario of high noise rate (80%), demonstrating the high robustness of our scheme to high-ratio noise. * Although MetaLabelNet uses part of clean data, i.e., meta-data for the relabeling of training samples, the meta-network is only trained on clean datasets and may overfit to these data and lead to the biased soft label for other training samples, which may affect the classifier performance. To solve this problem, CoNet-MS utilizes the meta-net to reweight the loss value between the pseudo-label and the observed label of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Ratio (\%)** & **20** & **40** & **50** & **60** & **80** \\ \hline Conventional & & & & & \\ Cross Entropy & 82.86 & 77.84 & 72.06 & 67.62 & 40.02 \\ \hline Co-teaching [9] & 85.88 & 81.02 & 75.84 & 70.28 & 43.88 \\ \hline MetaLabelNet [10] & 83.42 & 77.74 & 75.32 & 71.2 & 49.98 \\ \hline L2B [11] & 89.44 & 87.86 & 85.99 & 77.99 & 66.09 \\ \hline
**Ours (CoNet-MS)** & **91.3** & **89.61** & **88.80** & **80.78** & **69.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy (%) of all schemes under different levels of uniform noise on the CIFAR-10 dataset. Figure 2: Illustration of the used function of high-confidence proportion \(P(t)\) over epoch \(t\). the training sample while filtering samples through the cross dual-branch structure. As a result, the classification accuracy of the final model is improved by nearly 20% compared to the former in the case of an 80% noise rate. * The co-teaching method utilizes the \"small loss\" principle to filter out samples with high confidence and achieves good noise immunity by filtering potentially noisy samples from two networks with different parameters. However, simply using small loss to select training samples may result in leftover noise labels, and even worse, we cannot use the abundant samples with noisy labels, which may negatively affect the model. To address this issue, CoNet-MS proposed implicitly realizes the discarding of noisy samples by dynamically weighting the loss function to reduce the impact of lower confidence samples when updating the network. In addition, CoNet-MS also performs label estimation, which further improves the accuracy of the model by correcting the sample labels. Figure 3 shows the confusion matrix of our work under the uniform noise rate of 50%. The results demonstrate that our work effectively learns the correct distribution of samples. The diagonal cells in Figure 3 indicate the percentage of samples that are correctly classified by our scheme, while the non-diagonal cells mean the percentage of misclassified samples. Obviously, under a high noise ratio, e.g., 50%, our scheme can achieve outstanding classification performance.