# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Ablation Experiments  
To evaluate the effectiveness of feature labels and predictive labels, we conducted ablation experiments. The settings and outcomes of the ablation experiment on the CIFAR10 dataset can be found in Table 8. All models employed in these experiments were derived from the ResNet architecture.  
The test accuracy of CIFAR10 across different epochs is depicted in Figure 1. In the majority of experimental settings, the test accuracy is lower when the feature tag is not utilized compared to when it is employed. For Instance-20%, the classification accuracy is merely 0.02% higher than that of the FPL algorithm, which may be influenced by incidental factors. In the scenario of Tridiagonal-20%, the impact of solely incorporating prediction labels is notably more pronounced. Therefore, enhancements are required for the FPL algorithm in this type of noise. Nonetheless, we also computed the average classification accuracy for each ablation experiment configuration, revealing that our method consistently attained significant advantages in average accuracy. Moreover, the method without predicted labels exhibits a noticeable decline after 30 epochs, whereas our method remains more stable. This pattern is especially conspicuous when confronted with higher noise rates, suggesting that our proposed method excels under such circumstances.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Noise Type** & \multicolumn{4}{c}{**Asym**} \\ \hline \multirow{5}{*}{im-MNIST} & **Setting** & **20\%** & **30\%** & **40\%** & **45\%** \\ \cline{2-6}  & CE & \(90.66\pm 0.21\) & \(85.40\pm 0.25\) & \(80.40\pm 0.12\) & \(76.54\pm 0.20\) \\  & GCE & \(93.07\pm 0.11\) & \(87.94\pm 0.13\) & \(84.06\pm 0.12\) & \(78.84\pm 0.15\) \\  & APL & \(98.44\pm 0.05\) & \(98.02\pm 0.10\) & \(94.68\pm 0.09\) & \(89.50\pm 0.22\) \\  & ELR & \(95.66\pm 0.09\) & \(93.50\pm 0.13\) & \(89.28\pm 0.15\) & \(87.61\pm 0.14\) \\  & CDR & \(98.45\pm 0.11\) & \(98.44\pm 0.11\) & \(96.15\pm 0.48\) & \(94.24\pm 0.78\) \\  & PES & \(97.60\pm 0.08\) & \(96.42\pm 0.10\) & \(95.03\pm 0.14\) & \(93.91\pm 0.15\) \\  & CTRR & \(98.37\pm 0.15\) & \(97.17\pm 0.24\) & \(95.96\pm 0.51\) & \(94.22\pm 0.26\) \\  & FPL & \(\textbf{98.55\pm 0.05}\) & \(\textbf{98.55\pm 0.05}\) & \(\textbf{98.53\pm 0.06}\) & \(\textbf{98.58\pm 0.04}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average test accuracy (%) on im-MNIST over the last 10 epochs. The best mean results are in bold.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Noise Type** & \multicolumn{4}{c}{**Asymmetric**} \\ \hline \multirow{5}{*}{im-SVHN} & **Setting** & **20\%** & **30\%** & **40\%** & **45\%** \\ \cline{2-6}  & CE & \(83.84\pm 0.34\) & \(73.93\pm 0.49\) & \(59.44\pm 0.40\) & \(54.17\pm 0.50\) \\  & GCE & \(85.62\pm 0.23\) & \(76.35\pm 0.28\) & \(61.82\pm 0.31\) & \(57.22\pm 0.29\) \\  & APL & \(82.44\pm 0.05\) & \(71.84\pm 0.08\) & \(59.93\pm 0.14\) & \(53.74\pm 0.15\) \\  & ELR & \(80.73\pm 2.08\) & \(69.33\pm 0.10\) & \(58.27\pm 0.15\) & \(54.62\pm 0.18\) \\  & CDR & \(81.52\pm 2.08\) & \(72.58\pm 1.88\) & \(60.23\pm 2.74\) & \(55.10\pm 2.25\) \\  & PES & \(87.77\pm 0.06\) & \(74.56\pm 0.13\) & \(61.86\pm 0.18\) & \(56.64\pm 0.16\) \\  & CTRR & \(93.88\pm 0.41\) & \(89.64\pm 0.23\) & \(80.07\pm 0.36\) & \(73.59\pm 0.39\) \\  & FPL & \(\textbf{95.13\pm 0.04}\) & \(\textbf{95.18\pm 0.05}\) & \(\textbf{95.01\pm 0.05}\) & \(\textbf{94.99\pm 0.04}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average test accuracy (%) on im-SVHN over the last 10 epochs. The best mean results are in bold.