# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Experimental Setup
#### 4.1.5 Parameter Analysis  
\(\alpha\), \(\beta\), and \(\gamma\), respectively, represent the confidence proportion of the last round of pseudo-labels after the update, the confidence proportion of the prediction labels after the update, and the confidence proportion of the feature labels after the update. According to the concept of exponential moving average, the sum of these three parameters should equal 1. When the confidence of prediction labels and feature labels is high, the updated pseudo-labels will overly rely on the overall model performance, increasing the risk of overfitting in later training stages. In most scenarios, aside from the original clean labels, there is no guarantee that the corrected pseudo-labels are accurate, underscoring the critical importance of setting these three parameters thoughtfully. In this section, we will set three sets of hyperparameters, namely \(\alpha=0.8\), \(\beta=0.1\), \(\gamma=0.1\); \(\alpha=0.9\), \(\beta=0.09\), \(\gamma=0.01\); and \(\alpha=0.9\), \(\beta=0.099\), \(\gamma=0.001\). Experiments were performed on the CIFAR100 dataset, and the results of the experiments are shown in Table 2.  
According to the experimental findings, the highest test accuracy across the four noise types is achieved when setting the parameters to \(\alpha=0.9\), \(\beta=0.099\), and \(\gamma=0.001\). Due to the vast number of classes in CIFAR100, the classifier requirements are relatively stricter compared to other datasets, increasing the chance of feature learning errors. Hence, a decreased ratio of coefficients associated with feature labels proves advantageous. However, a lack of fine-tuning based on feature labeling can likewise lead to lower test accuracy. Based on the experimental settings mentioned above, we established the particular parameter configurations for four baseline datasets and one real dataset, respectively. For the MNIST dataset, we set \(\alpha=0.9\), \(\beta=0.09\), and \(\gamma=0.01\) for all four noise types. For the SVHN dataset, we set \(\alpha=0.9\), \(\beta=0.099\), and \(\gamma=0.001\). For the CIFAR10 dataset, we used the same parameter settings for symmetric noise, pair flip noise, and tridiagonal noise, which were \(\alpha=0.9\), \(\beta=0.09\), and \(\gamma=0.01\). For instance noise on CIFAR10, we set \(\alpha=0.9\), \(\beta=0.099\), and \(\gamma=0.001\). For the CIFAR100 dataset, we set \(\alpha=0.9\), \(\beta=0.099\), and \(\gamma=0.001\). For the Clothing1M dataset, we used the parameter settings of \(\alpha=0.8\), \(\beta=0.19\), and \(\gamma=0.01\) for all four noise types. Finally, for both im-SVHN and im-MNIST datasets, we used the same parameter settings of \(\alpha=0.9\), \(\beta=0.09\), and \(\gamma=0.01\) for all four noise types. To provide a clearer representation, we organized the specific parameters in a table format. Please refer to Table 3 for detailed information. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Settings**} & \multicolumn{2}{c}{**Symmetric**} & \multicolumn{2}{c}{**Pair flip**} & \multicolumn{2}{c}{**Tridiagonal**} & \multicolumn{2}{c}{**Instance**} \\ \(\mathbf{\alpha}\) & \(\beta\) & \(\gamma\) & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** \\ \hline
0.8 & 0.1 & 0.1 & 70.76 \(\pm\) 0.05 & 66.64 \(\pm\) 0.09 & 73.14 \(\pm\) 0.12 & 71.89 \(\pm\) 0.10 & 73.19 \(\pm\) 0.06 & 72.28 \(\pm\) 0.11 & 71.48 \(\pm\) 0.09 & 70.17 \(\pm\) 0.10 \\
0.9 & 0.09 & 0.01 & 71.51 \(\pm\) 0.06 & 67.73 \(\pm\) 0.07 & 73.15 \(\pm\) 0.05 & 71.84 \(\pm\) 0.08 & 73.20 \(\pm\) 0.05 & 72.55 \(\pm\) 0.06 & 71.37 \(\pm\) 0.06 & 70.28 \(\pm\) 0.05 \\
0.9 & 0.099 & 0.001 & **71.55 \(\pm\) 0.08** & **67.84 \(\pm\) 0.04** & **73.43 \(\pm\) 0.09** & **72.35 \(\pm\) 0.09** & **73.2 \(\pm\) 0.03** & **72.62 \(\pm\) 0.07** & **71.49 \(\pm\) 0.05** & **70.34 \(\pm\) 0.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average test accuracy (%) for the last 10 epochs with different hyperparameter settings on CIFAR100. The best mean results are in bold.