# ReliaMatch: Semi-Supervised Classification with Reliable Match
## 4 Experiments
### Experimental Results and Analysis  
In this section, we compared ReliaMatch with other common semi-supervised learning methods on CIFAR10 and SVHN datasets, including Pseudo-Label [14], LP-MT [13], PL-CB [16], Curriculum Labeling [33], FlexMatch [41] based on pseudo-labeling, \(\pi\) Model [10], Temporal Ensembling [10], Mean Teacher [15], VAT [11], Ladder Net [9], ICT [42] based on consistency regularization, and MixMatch [17] based on strong mixup. We also compared ReliaMatch with Meta-Semi [43], a meta-learning based SSL algorithm. Additionally, the reliability of ReliaMatch can be assessed by the Test Error Rate(%) and the Error Rate corresponding to different numbers of labeled samples: the lower Test Error Rates on different networks and datasets can prove the effectiveness of ReliaMatch; reducing the number of labeled samples without a significant increase in Error Rate can demonstrate the robustness of ReliaMatch. From the results shown in Tables 1 and 2, it can be seen that, compared to other semi-supervised learning methods, ReliaMatch considers issues such as feature and label noise, filters out semantically ambiguous features and unreliable pseudo-labels, and thus performs better. Specifically, the ReliaMatch method uses the same WideResNet-28 network as advanced semi-supervised classification methods proposed by previous researchers. On the CIFAR-10 dataset, this method used 4000 labeled data for testing, and achieved a test error rate of only 5.86%. In comparison, the test error rate of the Pseudo-Label method was 17.78%, Curriculum Labeling method was 8.92%, and PL-CB method was 6.28%. In addition, FlexMatch was slightly better than ReliaMatch, which had a test error rate of only 4.19% on CIFAR-10. On the SVHN dataset, the ReliaMatch method used only 1000 labeled data for testing, achieving a test error rate of 4.04%, significantly better than the test error rates of Pseudo-Label (7.62%), Curriculum Labeling (5.65%), and FlexMatch method (6.72%). In addition, the ReliaMatch method uses the same CNN-13 network as advanced semi-supervised classification methods proposed by previous researchers. On the CIFAR-10 dataset, using 4000 labeled data for testing, the test error rate of ReliaMatch method was 7.42%, which is lower than the test error rates of Pseudo-Label methods (LP-MT, Curriculum Labeling) and consistency regularization methods (Ladder Net, Temporal Ensembling). We also performed an evaluation on the CIFAR-100 [37] dataset. ReliaMatch achieved 38.67% and 37.28% Test Error Rate on CIFAR-100 for WidResNet-28 and CNN-13. It is worth noting that it was a large difference from the effect on the other two datasets, which may be due to CIFAR-100 being more complex, with more categories, and the maximum predictive probability of each category may have been significantly different, making the global dynamic threshold not adapted. One common way to evaluate semi-supervised classification algorithms is by varying the size of the labeled dataset. By reducing the number of available labeled samples, it is possible to better simulate real-world scenarios. As shown in Figure 5, on the CIFAR-10 and SVHN datasets, we tested the error rates of the ReliaMatch method using the WideResNet-28 network under different numbers of labeled samples. We used datasets with 500, 1000, 2000, and 4000 labeled samples, respectively, and only changed the number of labeled samples during each training while keeping other hyperparameters the same as when using 4000 labeled samples. The experimental results show that the classification performance of the ReliaMatch method does not significantly degrade under different numbers of labeled samples. This indicates that the ReliaMatch method has good robustness and can provide stable performance even with very limited labeled data, which is crucial for practical applications since, in many cases, only very limited labeled data can be obtained. Therefore, these results