## 4 Experiments  
In this section, a series of experimental results are presented. We test the effectiveness of our proposed algorithm on three benchmark datasets, including MNIST [29], CIFAR-10, and CIFAR-100 [30]. The main information of these datasets is shown in Table 3. In past research, these datasets were commonly used to assess learning with noisy labels. [9; 31; 32]. We compare JoSDW with the following state-of-the-art algorithms, implement all methods with default parameters through PyTorch, and perform all experiments on NVIDIA 2080 ti. The experimental data comes from the following three datasets: Mnist, CIFAR-10, CIFAR-100. In order to explore the performance in different noise cases, we use the following four noise ratios: Symmetry-20%, Symmetry-50%, Symmetry-80%, Asymmetry-40%. Figure 2 is an example of noise ratios. The symmetric noise, which is also called random or uniform noise, is a common setup of noisy label experiments. M represents the number of classes, and \(\alpha\) represents the noise ratio. The probability of a true label is \(P_{true}=1-\alpha\). The probability of a noisy label is \(P_{noisy}=\alpha\). The probability of each noisy label is \(P_{noise}^{i}=\frac{\alpha}{M-1}\). The asymmetric noise is closer to a real-world label noise because of flipping. For example, on Mnist, the asymmetric noise maps 2\(\rightarrow\)4, 3\(\rightarrow\)1, 6\(\rightarrow\)3. We utilize a two-layer MLP for MNIST and a seven-layer CNN network architecture for CIFAR-10 and CIFAR-100 in terms of the network structure. The network architecture of MLP and CNN is shown in Table 4. Regarding the optimizer, we use an Adam optimizer with momentum = 0.9. The initial learning rate is 0.001. The batch size is set to 128. We run 200 epochs totally, with the learning rate gradually decaying to 0 from 80 to 200 epochs. Figure 2: Example of noise transition matrices (taking 6 classes). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **\# of Class** & **\# of Train** & **\# of Test** & **Size** \\ \hline Mnist & 10 & 60,000 & 10,000 & 28 \(\times\) 28 \\ CIFAR-10 & 10 & 50,000 & 10,000 & 32 \(\times\) 32 \\ CIFAR-100 & 100 & 50,000 & 10,000 & 32 \(\times\) 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Image resolution of common data sets for label noise. Table 5 compares the precision accuracy of various algorithms. In the conventional Symmetry-20% case and Symmetry-50% case, all the new methods are clearly superior to the standard method, which proves their robustness. However, when the noise label reaches the symmetry-80% case, the performance of decoupling and co-teaching+ based on disagreement declines substantially, while JoSDW is considerably better than the other methods. We plot label precision vs. epochs on the right side of Figures 3-6. These experiments demonstrate the characteristics of JoSDW. Although the peak precision accuracy of JoSDW training is not as high as other algorithms, the existence of dynamic weights  
Figure 5: Results on MNIST dataset. Noise settings, Symmetry-80%. **Left**: precision accuracy (%) vs. epochs; **Right**: label precision (%) vs. epochs. Figure 6: Results on MNIST dataset. Noise settings, Asymmetry-40%. **Left**: precision accuracy (%) vs. epochs; **Right**: label precision (%) vs. epochs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Noise Settings** & **Standard** & **F-Correction** & **Decoupling** & **Co-Teaching** & \begin{tabular}{c} **Co-** \\ **Teaching+** \\ \end{tabular} & **JoCOR** & **JoSDW** \\ \hline \begin{tabular}{c} Symmetry- \\ 20\% \\ Symmetry- \\ 50\% \\ Symmetry- \\ 80\% \\ Asymmetry- \\ 40\% \\ \end{tabular} & 79.56 & 95.38 & 93.16 & 95.1 & 97.81 & 98.06 & 98.24 \\ \hline \
\begin{tabular}{c} Symmetry- \\ 20\% \\ Symmetry- \\ 80\% \\ \end{tabular} & 52.66 & 92.74 & 69.79 & 89.82 & 95.8 & 96.64 & 97.23 \\ \hline
\begin{tabular}{c} Symmetry- \\ 20\% \\ Symmetry- \\ 80\% \\ \end{tabular} & 23.43 & 72.96 & 28.51 & 79.73 & 58.92 & 84.89 & 90.31 \\ \hline \hline \end{tabular}
\end{table}