# Dual-Training-Based Semi-Supervised Learning with Few Labels
## 4 Experiments
### Comparison with State-of-the-Art Methods  
We compared our proposed method with several state-of-the-art methods, including \(\pi\)-Model [42], Mean-Teacher [18], MixMatch [19], FixMatch [16], CoMatch [22], SimMatch [23], ICL-SSL [24] and \(p\)-Mix [25]. Among them, FixMatch [16] first achieved great success by combining the advantages of pseudo-labeling methods and consistency-based methods. CoMatch [22] and SimMatch [23] introduced the information of categories and features to improve FixMatch. ICL-SSL [24] and \(p\)-Mix [25] further considered fewer labeled data and improved the model performance.  
We present the comparison results of our method and other compared methods on Cifar10 and Cifar100 with different numbers of labeled data in Table 3. It can be observed that our method outperformed all the other compared methods with different numbers of labeled data. For instance, our method achieved an accuracy of 92.67% with only 20 labeled data on Cifar10, which is 0.72% and 3.94% higher than \(p\)-Mix [25] and ICL-SSL [24], respectively. For Cifar100, a more complex dataset with more categories, our method can also achieve the best results, as can be seen by the significant margin between our proposed method and other compared methods. The results demonstrate that our method has the ability to achieve better results with fewer labels, which helps reduce the cost of annotating labels.  
We further investigated the classification accuracy of our method with normal numbers of labeled data on the SVHN dataset. Table 4 illustrates the results of experiments conducted on the SVHN dataset with 250, 500 and 1000 labeled data. As shown in Table 4, our method can still achieve the best result. For instance, our method outperformed the \(p\)-Mix [25] with an accuracy of 0.26% on the SVHN dataset with 1000 labeled data, which strongly demonstrates the effectiveness of our method with normal numbers of labeled data.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Cifar10**} & \multicolumn{3}{c}{**Cifar100**} \\ \cline{2-7}  & **20 Labels** & **30 Labels** & **40 Labels** & **200 Labels** & **400 Labels** & **800 Labels** \\ \hline \(\pi\)-Model [42] & - & - & - & 8.53 \(\pm\) 0.25 & 11.67 \(\pm\) 0.37 & 17.64 \(\pm\) 1.0 \\ MeanTeacher [18] & 21.79 \(\pm\) 0.57 & 24.51 \(\pm\) 0.35 & 24.93 \(\pm\) 0.62 & 7.11 \(\pm\) 0.06 & 11.54 \(\pm\) 0.28 & 17.82 \(\pm\) 0.09 \\ MixMatch [19] & 38.51 \(\pm\) 8.48 & 50.10 \(\pm\) 5.81 & 59.08 \(\pm\) 3.04 & 4.55 \(\pm\) 0.45 & 17.68 \(\pm\) 0.07 & 26.75 \(\pm\) 1.13 \\ FixMatch [16] & 72.63 \(\pm\) 5.37 & 86.65 \(\pm\) 3.56 & 89.69 \(\pm\) 4.58 & 9.31 \(\pm\) 0.08 & 24.44 \(\pm\) 0.35 & 28.12 \(\pm\) 0.30 \\ CoMatch [22] & 83.43 \(\pm\) 9.20 & 88.68 \(\pm\) 3.79 & 90.14 \(\pm\) 2.86 & 22.39 \(\pm\) 1.35 & 29.60 \(\pm\) 0.88 & 37.00 \(\pm\) 0.59 \\ SimMatch [23] & 78.13 \(\pm\) 6.12 & 90.01 \(\pm\) 4.15 & 91.05 \(\pm\) 3.11 & 25.43 \(\pm\) 1.98 & 38.66 \(\pm\) 1.61 & 52.41 \(\pm\) 0.76 \\ ICL-SSL [24] & 88.73 \(\pm\) 5.69 & 90.30 \(\pm\) 3.10 & 91.78 \(\pm\) 2.23 & 14.06 \(\pm\) 0.52 & 26.52 \(\pm\) 1.20 & 33.81 \(\pm\) 0.63 \\ \(p\)-Mix [25] & 91.95 \(\pm\) 5.95 & 92.64 \(\pm\) 1.69 & 93.65 \(\pm\) 0.10 & 25.25 \(\pm\) 3.28 & 38.01 \(\pm\) 2.96 & 48.24 \(\pm\) 3.91 \\ Ours & **92.67 \(\pm\) 2.59** & **93.21 \(\pm\) 2.45** & **94.71 \(\pm\) 0.26** & **29.43 \(\pm\) 2.02** & **43.90 \(\pm\) 1.49** & **53.52 \(\pm\) 0.54** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with state-of-the-art methods in test accuracy (%) on Cifar10 and Cifar100 datasets with different numbers of labeled data. The bold represents the best result.