# ReliaMatch: Semi-Supervised Classification with Reliable Match
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **CIFAR-10 (\(N_{L}\) = 4000)** & **SVHN (\(N_{L}\) = 1000)** \\ \hline PL \(\uparrow\) & 17.78 \(\pm\) 0.57 & 7.62 \(\pm\) 0.29 \\ \hline Curriculum Labeling \(\uparrow\) & 8.92 \(\pm\) 0.03 & 5.65 \(\pm\) 0.11 \\ \hline PL-CB \(\uparrow\) & 6.28 \(\pm\)0.30 & - \\ \hline \(\pi\) Model \(\uparrow\) & 16.37 \(\pm\) 0.63 & 7.19 \(\pm\) 0.27 \\ \hline Mean Teacher & 10.36 \(\pm\) 0.28 & 5.65 \(\pm\) 0.47 \\ \hline VAT \(\uparrow\) & 13.86 \(\pm\) 0.27 & 5.63 \(\pm\) 0.20 \\ \hline VAT+EntMin \(\uparrow\) & 13.13 \(\pm\) 0.39 & 5.35 \(\pm\) 0.19 \\ \hline ICT \(\uparrow\) & 7.66 \(\pm\) 0.17 & 3.53 \(\pm\) 0.07 \\ \hline MixMatch \(\uparrow\) & 6.24 \(\pm\) 0.06 & 3.27 \(\pm\) 0.31 \\ \hline FlexMatch \(\uparrow\) & 4.19 \(\pm\) 0.01 & 6.72 \(\pm\) 0.30 \\ \hline Meta-Semi \({}^{*}\) & 6.10 \(\pm\) 0.10 & - \\ \hline
**ReliaMatch \(*\)** & **5.86 \(\pm\) 0.12** & **4.04 \(\pm\) 0.08** \\ \hline \end{tabular} \(N_{L}\) represents the number of labeled samples in the training set, \(\uparrow\) indicates that the result has been reported in the literature [33], \(\dagger\) indicates result has been reported in [44], \(*\) indicates the result has been reported in [45] and \(*\) represents the average of 5 runs of the method proposed in this paper.  
\end{table} 
Table 1: Comparison of Test Error Rate (%) of WideResNet-28 with Different Semi-supervised Methods. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **CIFAR-10 (\(N_{L}\) = 4000)** & **SVHN (\(N_{L}\) = 1000)** \\ \hline LP-MT \(\uparrow\) & 10.16 \(\pm\) 0.28 & - \\ \hline Curriculum Labeling \(\uparrow\) & 9.81 \(\pm\) 0.22 & 4.75 \(\pm\) 0.28 \\ \hline Ladder Net \(\uparrow\) & 12.16 \(\pm\) 0.31 & - \\ \hline Temporal Ensembling \(\uparrow\) & 12.16 \(\pm\) 0.24 & 4.42 \(\pm\)0.16 \\ \hline
**ReliaMatch \(*\)** & **7.42 \(\pm\)0.05** & **7.13 \(\pm\) 0.28** \\ \hline \end{tabular} \(N_{L}\) represents the number of labeled samples in the training set, \(\uparrow\) indicates that the result has been reported in the literature [33], and \(*\) represents the average of five runs of the proposed method in this paper.  
\end{table} 
Table 2: Comparison of Test Error Rate (%) of Different Semi-supervised Methods Using CNN-13. suggest that the ReliaMatch method is an effective semi-supervised classification algorithm that can be useful in practical applications. In addition, we also investigated the effectiveness of the feature filtering module and pseudo-label filtering module in the ReliaMatch method. As shown in Table 3, we separately or collectively removed these two modules and evaluated their impact on the method performance by applying data augmentation during training. The experiments were conducted on the CIFAR-10 dataset with 4000 labeled samples, and the WideResNet-28 was used as the backbone network. After analyzing Table 3, we have reached the following conclusions: the pseudo label filtering module has a significant impact on the performance of the ReliaMatch method, as the removal of this module leads to an increase in model error rate from 5.86 to 9.12. This indicates that the pseudo label filtering module can remove low-confidence pseudo labels, thereby reducing noise and improving model performance. In contrast, the effect of the feature filtering module is not as significant as that of the pseudo label filtering module, but it still contributes to improving model performance. Its removal results in a model error rate increase from 5.86 to 6.74, indicating that the feature filtering module can select high-quality features and thus improve the model's performance. Additionally, removing both feature and pseudo label filtering modules causes a sharp increase in model error rate, from 5.86 to 16.9. This demonstrates that both feature and pseudo label filtering modules make important contributions to the performance of the ReliaMatch method, and both are necessary. Therefore, both feature and pseudo label filtering modules play important roles in the ReliaMatch method.