# DYMatch: Semi-Supervised Learning with Dynamic Pseudo Labeling and Feature Consistency
## 4 Experiments
### Results
#### 4.3.1 Experimental Results for Standard SSL Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**SVHN**} & \multicolumn{4}{c}{**400 Labels**} & \multicolumn{1}{c}{**250 Labels**} & \multicolumn{1}{c}{**500 Labels**} & \multicolumn{1}{c}{**1000 Labels**} & \multicolumn{1}{c}{**4000 Labels**} \\ \hline Mean Teacher [27] & 36.09 \(\pm\) 3.98 & 6.45 \(\pm\) 2.43 & 3.82 \(\pm\) 0.17 & 3.75 \(\pm\) 0.10 & 3.39 \(\pm\) 0.11 \\ Pseudo-Label [16] & 64.61 \(\pm\) 5.60 & 21.16 \(\pm\) 0.88 & 14.35 \(\pm\) 0.37 & 10.19 \(\pm\) 0.41 & 5.71 \(\pm\) 0.07 \\ MixMatch [7] & 30.60 \(\pm\) 8.39 & 3.78 \(\pm\) 0.26 & 3.64 \(\pm\) 0.46 & 3.27 \(\pm\) 0.31 & 2.89 \(\pm\) 0.06 \\ ReMixMatch [8] & 24.04 \(\pm\) 9.13 & 3.10 \(\pm\) 0.50 & 3.02 \(\pm\) 0.33 & 2.83 \(\pm\) 0.30 & 2.42 \(\pm\) 0.09 \\ UDA [18] & 5.12 \(\pm\) 4.27 & 2.74 \(\pm\) 2.76 & 2.55 \(\pm\) 0.09 & 2.35 \(\pm\) 0.07 & 2.28 \(\pm\) 0.10 \\ FixMatch [9] & 3.81 \(\pm\) 1.18 & 2.48 \(\pm\) 0.38 & 2.35 \(\pm\) 0.05 & 2.28 \(\pm\) 0.11 & 2.20 \(\pm\) 0.09 \\ FlexMatch [10] & 8.19 \(\pm\) 3.20 & 6.59 \(\pm\) 2.29 & 6.79 \(\pm\) 1.20 & 6.72 \(\pm\) 0.30 & 4.45 \(\pm\) 0.12 \\ Semi-Clustering [48] & 3.09 \(\pm\) 0.54 & 2.30 \(\pm\) 0.03 & 2.29 \(\pm\) 0.03 & 2.26 \(\pm\) 0.12 & 2.15 \(\pm\) 0.12 \\ DoubleMatch [23] & 16.50 \(\pm\) 13.73 & 2.41 \(\pm\) 0.53 & 2.50 \(\pm\) 0.23 & 2.25 \(\pm\) 0.09 & 2.15 \(\pm\) 0.09 \\ FeatMatch [22] & 4.01 \(\pm\) 0.13 & 3.34 \(\pm\) 0.19 & 3.08 \(\pm\) 0.12 & 3.10 \(\pm\) 0.06 & 2.62 \(\pm\) 0.08 \\ MPL [19] & 9.33 \(\pm\) 8.02 & 2.29 \(\pm\) 0.03 & 2.30 \(\pm\) 0.12 & 2.28 \(\pm\) 0.07 & 2.18 \(\pm\) 0.12 \\ SimMatch [47] & 3.19 \(\pm\) 0.74 & 2.26 \(\pm\) 0.21 & 2.14 \(\pm\) 0.20 & 2.08 \(\pm\) 0.04 & 2.97 \(\pm\) 0.07 \\ SoftMatch [11] & 2.31 \(\pm\) 0.00 & **2.15 \(\pm\) 0.05** & 2.16 \(\pm\) 0.10 & 2.08 \(\pm\) 0.04 & 1.93 \(\pm\) 0.00 \\on CIFAR-10 and CIFAR-100 datasets, respectively. The classification accuracy of DYMatch was marginally lower by 0.03% than that of SoftMatch when 250 labeled samples were used on SVHN. However, with utilizing other settings of the number of the labeled data, DYMatch can achieve better performance compared to SoftMatch. In general, most models obtained better classification performance on the SVHN dataset, resulting in a relatively small performance gap among some outstanding models. As shown in Table 4, for the STL-10 dataset, DYMatch still achieved very competitive classification performance. DYMatch can obtain the lowest error rate when using 1000 labeled data. And when only 40 labeled data are available, DYMatch's error rate is only 2.02% higher than FreeMatch. \begin{table}
\begin{tabular}{c c c} \hline  & \multicolumn{2}{c}{**STL10**} \\ \hline
**Model** & **40 Labels** & **1000 Labels** \\ \hline Mean Teacher [27] & - & 21.43 \(\pm\) 2.39 \\ Pseudo-Label [16] & 74.76 \(\pm\) 0.99 & 27.99 \(\pm\) 0.80 \\ MixMatch [7] & 54.93 \(\pm\) 0.96 & 10.41 \(\pm\) 0.61 \\ ReMixMatch [8] & 32.12 \(\pm\) 6.24 & 5.23 \(\pm\) 0.45 \\ UDA [18] & 37.42 \(\pm\) 8.44 & 7.66 \(\pm\) 0.56 \\ FixMatch [9] & 35.97 \(\pm\) 4.14 & 5.17 \(\pm\) 0.63 \\ FlexMatch [10] & 29.15 \(\pm\) 4.16 & 5.77 \(\pm\) 0.23 \\ Semi-Clustering [48] & 40.90 \(\pm\) 0.23 & 4.78 \(\pm\) 0.29 \\ DoubleMatch [23] & 34.34 \(\pm\) 0.34 & 4.46 \(\pm\) 0.20 \\ FeatMatch [22] & 51.26 \(\pm\) 0.33 & 6.94 \(\pm\) 0.32 \\ MPL [19] & 35.76 \(\pm\) 3.29 & 6.45 \(\pm\) 0.29 \\ SimMatch [47] & 37.57 \(\pm\) 0.23 & 5.63 \(\pm\) 0.35 \\ SoftMatch [11] & 21.42 \(\pm\) 3.48 & 5.73 \(\pm\) 0.24 \\ FreeMatch [12] & **15.56 \(\pm\) 0.55** & 5.63 \(\pm\) 0.15 \\ \hline DYMatch (ours) & 17.58 \(\pm\) 0.67 & **5.35 \(\pm\) 0.12** \\ \hline \end{tabular}
\end{table} 
Table 4: The Error Rates for STL10 on Two Different Numbers of Labelled Images. The best result is in bold.