# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 5 Experimental Results
### Ablation Studies
#### 5.1.2 Proportions of Labeled Data  
This part of the experiment is designed to analyze the improvements caused by increasing the proportion of labeled data and find a ratio that balances data volume and accuracy. Specifically, the percentage of labeled data is gradually expanded, beginning with 5%, and increasing to 10%, 20%, and finally 40%, which guarantees an inclusion logic in spite of expanding proportions of labeled data, and minimizes the impact of data diversity \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **IoU** \\ \hline VAT & 0.948 & 0.953 & 0.966 & 0.960 & 0.865 \\ Dropout & 0.950 & 0.950 & 0.971 & 0.960 & 0.862 \\ Feature noise & 0.950 & 0.952 & 0.968 & 0.960 & 0.864 \\ Salt noise & 0.952 & 0.955 & 0.967 & 0.961 & 0.857 \\ Color jittering & 0.949 & 0.951 & 0.967 & 0.959 & 0.861 \\ Lighting & 0.953 & 0.956 & 0.968 & 0.963 & 0.868 \\ All & 0.954 & 0.957 & 0.969 & 0.963 & 0.871 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on different perturbations of aux-encoders in Cityscapes dataset with 40% labeled data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & \multicolumn{2}{c}{**Cityscapes**} & \multicolumn{2}{c}{**CamVid**} \\
**aux-En** & **Aux-De** & **Accuracy** & **IoU** & **Accuracy** & **IoU** \\ \hline \(\times\) & \(\times\) & 0.931 & 0.825 & 0.915 & 0.822 \\ \(\times\) & \(\times\) & 0.952 & 0.857 & 0.930 & 0.824 \\ \(\times\) & \(\checkmark\) & 0.951 & 0.845 & 0.934 & 0.836 \\ \(\checkmark\) & \(\checkmark\) & 0.954 & 0.871 & 0.939 & 0.865 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results on models with different auxiliary modules with 40% labeled data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & \multicolumn{2}{c}{**Cityscapes**} & \multicolumn{2}{c}{**CamVid**} \\
**aux-En** & **Aux-De** & **Accuracy** & **IoU** & **Accuracy** & **IoU** \\ \hline \(\times\) & \(\times\) & 0.931 & 0.825 & 0.915 & 0.822 \\ \(\checkmark\) & \(\times\) & 0.952 & 0.857 & 0.930 & 0.824 \\ \(\times\) & \(\checkmark\) & 0.951 & 0.845 & 0.934 & 0.836 \\ \(\checkmark\) & \(\checkmark\) & 0.954 & 0.871 & 0.939 & 0.865 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results on models with different auxiliary modules with 40% labeled data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **IoU** \\ \hline VAT & 0.919 & 0.907 & 0.975 & 0.938 & 0.832 \\ Dropout & 0.928 & 0.904 & 0.981 & 0.943 & 0.849 \\ Feature noise & 0.932 & 0.924 & 0.975 & 0.948 & 0.848 \\ Salt noise & 0.926 & 0.904 & 0.985 & 0.942 & 0.843 \\ Color jittering & 0.925 & 0.908 & 0.981 & 0.943 & 0.838 \\ Lighting & 0.932 & 0.933 & 0.975 & 0.953 & 0.862 \\ All & 0.939 & 0.931 & 0.977 & 0.953 & 0.865 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on different perturbations of aux-encoders in Camvid dataset with 40% labeled data. caused by appending new labeled data. Moreover, in order to investigate convergence during training on two datasets, the loss curves with 40% labeled data are recorded in Figure 6.  
After setting the proportion of labeled data in the semi-supervised datasets, the following two experiments were conducted: fully supervised training on the labeled subset of the semi-supervised datasets and semi-supervised training on the whole of the semi-supervised datasets. All models use the same number of aux-encoders and aux-decoders, and the baseline is the result of fully supervised training on the original datasets with 100% of the data labeled. Figure 7 displays the line charts of the relationship between proportions of labeled data and IoU on two datasets and Figure 8 shows the corresponding visualization of the predictions by semi-supervised models on a certain input. It can be seen from Figure 7 that there is a significant gap between the IoU of fully supervised and semi-supervised methods when the proportions of labeled data are lower than 20%.