# DYMatch: Semi-Supervised Learning with Dynamic Pseudo Labeling and Feature Consistency
## 4 Experiments
### Results  
#### 4.3.1 Experimental Results for Standard SSL Datasets  
In this subsection, we presented the classification performance of DYMatch with other baseline methods on the standard SSL datasets from Tables 1-4. Results for Mean Teacher and Pseudo-Label with 40 labels per class are not included due to their poor performance on CIFAR-100. MPL, SoftMatch and FreeMatch obtained the best classification performance with few specific settings of the number of labeled data. However, DYMatch demonstrates good classification performance, outperforming other baseline methods on most labeled data settings on each standard SSL dataset. As shown from Tables 1-3, with the results on the CIFAR-10, CIFAR-100, and SVHN datasets, and DYMatch consistently obtains the good classification performance on most different settings of the numbers of labeled data. In experiments with 40 labeled data per class on CIFAR-10, the error rate of DYMatch was slightly higher than MPL by 0.07%, but it was lower than other baseline methods. DYMatch also performed worse than MPL, when employing 2500 labeled samples on CIFAR-100. Furthermore, as the quantity of labeled data decreases, the DYMatch gradually demonstrates its robust classification performance. When utilizing a very small number of labeled samples, for example, only 4 labels per class, DYMatch exhibited the lowest error rates, which were 1.73% and 0.46% lower than MPL \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{4}{c}{**40 Labels**} & \multicolumn{1}{c}{**250 Labels**} & \multicolumn{1}{c}{**500 Labels**} & \multicolumn{1}{c}{**2000 Labels**} & \multicolumn{1}{c}{**4000 Labels**} \\ \hline Mean Teacher [27] & 70.09 \(\pm\) 0.26 & 47.33 \(\pm\) 4.71 & 42.01 \(\pm\) 5.86 & 12.17 \(\pm\) 0.22 & 9.19 \(\pm\) 0.19 \\ Pseudo-Label [16] & 74.61 \(\pm\) 1.60 & 49.98 \(\pm\) 1.17 & 40.55 \(\pm\) 1.70 & 21.96 \(\pm\) 0.42 & 16.21 \(\pm\) 0.11 \\ MixMatch [7] & 36.19 \(\pm\) 6.48 & 11.08 \(\pm\) 0.87 & 9.65 \(\pm\) 0.94 & 7.03 \(\pm\) 0.15 & 6.24 \(\pm\) 0.06 \\ ReMixMatch [8] & 9.88 \(\pm\) 1.03 & 6.27 \(\pm\) 0.34 & 6.04 \(\pm\) 0.04 & 5.53 \(\pm\) 0.18 & 5.14 \(\pm\) 0.04 \\ UDA [18] & 10.62 \(\pm\) 3.75 & 5.43 \(\pm\) 0.96 & 4.80 \(\pm\) 0.09 & 4.73 \(\pm\) 0.14 & 4.32 \(\pm\) 0.08 \\ FixMatch [9] & 7.47 \(\pm\) 0.28 & 5.07 \(\pm\) 0.65 & 4.85 \(\pm\) 0.06 & 4.54 \(\pm\) 0.12 & 4.26 \(\pm\) 0.05 \\ FlexMatch [10] & 4.97 \(\pm\) 0.06 & 4.98 \(\pm\) 0.49 & 4.47 \(\pm\) 0.34 & 4.18 \(\pm\) 0.12 & 4.19 \(\pm\) 0.01 \\ Semi-Clustering [48] & 7.39 \(\pm\) 0.61 & 5.51 \(\pm\) 0.25 & 5.40 \(\pm\) 0.23 & 4.71 \(\pm\) 0.12 & 4.62 \(\pm\) 0.09 \\ DoubleMatch [23] & 14.02 \(\pm\) 5.71 & 5.56 \(\pm\) 0.42 & 5.12\(\pm\)0.43 & 4.66 \(\pm\) 0.23 & 4.65 \(\pm\) 0.17 \\ FeatMatch [22] & 7.88 \(\pm\) 0.52 & 7.50 \(\pm\) 0.64 & 6.21 \(\pm\) 0.11 & 4.95 \(\pm\) 0.20 & 4.91 \(\pm\) 0.18 \\ MPL [19] & 6.62 \(\pm\) 0.91 & 5.76 \(\pm\) 0.26 & 5.89 \(\pm\) 0.23 & 4.40 \(\pm\) 0.04 & **3.89 \(\pm\) 0.07** \\ SimMatch [47] & 5.18 \(\pm\) 0.15 & 4.99 \(\pm\) 0.13 & 4.62 \(\pm\) 0.21 & 4.22 \(\pm\) 0.14 & 4.31 \(\pm\) 0.01 \\ SoftMatch [11] & 5.06 \(\pm\) 0.02 & 4.84 \(\pm\) 0.10 & 4.66 \(\pm\) 0.12 & 4.33 \(\pm\) 0.10 & 4.27 \(\pm\) 0.02 \\ FreeMatch [12] & 4.97 \(\pm\) 0.09 & 4.85 \(\pm\) 0.10 & 4.57 \(\pm\) 0.10 & 4.21 \(\pm\) 0.05 & 4.14 \(\pm\) 0.02 \\ \hline DYMatch (ours) & **4.89 \(\pm\) 0.11** & **4.80 \(\pm\) 0.07** & **4.53 \(\pm\) 0.08** & **4.15 \(\pm\) 0.11** & 3.96 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Error Rates for CIFAR-10 with Five Different Numbers of Labelled Images. The best result is in bold.