# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 4 Discussion  
The paper introduces MAC-SSL, which proposes a novel approach to leverage the valuable information within unlabeled samples in semi-supervised learning tasks. Unlike traditional methods that rely on pseudo-labels or soft labels to enforce consistency between strongly and weakly augmented unlabeled images, MAC-SSL incorporates a moderate augmentation step. This step combines the model outputs of moderately augmented unlabeled images with those of weakly augmented unlabeled images to generate pseudo-labels. Subsequently, the model outputs of strongly augmented images are enforced to be consistent with the pseudo-labels. Additionally, inspired by MixMatch, MixUP is adopted \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CIFAR-10 (\(\mu\) = 5)**} \\ \cline{2-5}  & **40 Labels** & **500 Labels** & **1000 Labels** & **4000 Labels** \\ \hline MAC-SSL (base for CIFAR10) & 87.36 \(\pm\) 0.09 & **93.93 \(\pm\) 0.1** & **94.32 \(\pm\) 0.18** & **94.96 \(\pm\) 0.09** \\ MAC-SSL-II (base for SVHN) & **90.22 \(\pm\) 0.14** & 93.29 \(\pm\) 0.07 & 93.97 \(\pm\) 0.08 & 94.91 \(\pm\) 0.07 \\ MAC-SSL/MAC-SSL-II without temperature sharpening (T = 1) & – & – & 93.31 \(\pm\) 0.08 & – \\ MAC-SSL/MAC-SSL-II without MixUP & – & – & 91.88 \(\pm\) 0.11 & – \\ MAC-SSL/MAC-SSL-II with MixUP on labeled only & – & – & 90.07 \(\pm\) 0.14 & – \\ MAC-SSL/MAC-SSL-II with MixUP on unlabeled only & – & – & 93.97 \(\pm\) 0.35 & – \\ MAC-SSL/MAC-SSL-II with MixUP on separate labeled & – & – & 92.36 \(\pm\) 0.42 & – \\ MAC-SSL/MAC-SSL-II without distribution averaging & – & – & 91.6 \(\pm\) 0.18 & – \\ MAC-SSL/MAC-SSL-II without moderate augmentation & – & – & 94.18 \(\pm\) 0.24 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study results (a). All values indicate accuracy (%). \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**SVHN (\(\mu\) = 5)**} \\ \cline{2-4}  & **250 Labels** & **1000 Labels** \\ \hline MAC-SSL (base for CIFAR10) & 95.35 \(\pm\) 0.1 & 95.65 \(\pm\) 0.08 \\ MAC-SSL-II (base for SVHN) & **95.87 \(\pm\) 0.06** & **96.46 \(\pm\) 0.04** \\ MAC-SSL/MAC-SSL-II without temperature sharpening (T = 1) & – & 95.95 \(\pm\) 0.05 \\ MAC-SSL/MAC-SSL-II without MixUP & – & 94.83 \(\pm\) 0.12 \\ MAC-SSL/MAC-SSL-II with MixUP on labeled only & – & 94.02 \(\pm\) 0.13 \\ MAC-SSL/MAC-SSL-II with MixUP on unlabeled only & – & 96.17 \(\pm\) 0.06 \\ MAC-SSL/MAC-SSL-II with MixUP on separate labeled and unlabeled & – & 96.06 \(\pm\) 0.04 \\ MAC-SSL/MAC-SSL-II without distribution averaging & – & 96.44 \(\pm\) 0.04 \\ MAC-SSL/MAC-SSL-II without moderate augmentation & – & 95.50 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study results (b). All values indicate accuracy (%). For SVHN, MAC-SSL-II was the base. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CIFAR-10 (1000 Labels)**} \\ \cline{2-4}  & **\(\mu\) = 1** & **\(\mu\) = 3** & **\(\mu\) = 5** & **\(\mu\) = 7** & **\(\mu\) = 9** \\ \hline MAC-SSL & **89.47 \(\pm\) 0.14** & **93.18 \(\pm\) 0.08** & **94.32 \(\pm\) 0.18** & **94.55 \(\pm\) 0.08** & **96.46 \(\pm\) 0.04** \\ \hline MAC-SSL-II & 88.49 \(\pm\) 0.18 & 92.96 \(\pm\) 0.17 & 93.96 \(\pm\) 0.08 & 94.5 \(\pm\) 0.09 & 94.85 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results (c). All values indicate accuracy (%). to combine the outputs of unlabeled images with those of labeled images, augmenting the data and ensuring consistency between the model outputs of the re-augmented images and the newly generated pseudo-labels derived from mixed labels or pseudo-labels. The experimental results demonstrate the superior performance of MAC-SSL compared to existing methods, particularly in scenarios with limited labeled samples. MAC-SSL effectively leverages the valuable information within unlabeled samples, which other methods often overlook in their data augmentation techniques.