# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 5 Experimental Results
### Visual Comparison between Analyzed Methods  
As an example of the conducted visual comparisons, in the following are presented several figures that illustrate visual performance of the proposed BTS-SSL CycleGAN method against considered baselines.  
In Figures 4-6, visual examples are given for the proposed BTS-SSL CycleGAN vs. baseline algorithm comparisons, for 100% of the scarce domain data used: Real A (scarce domain) and Real B (target domain) correspond to image pair examples. Examples are  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Task/Dataset** & **Measure** & \(\mathbf{\mathcal{S}_{X}}\) & **pix2 pix** & **CycleGAN** & **SSL** & **BTS** & **SSL+BTS** & \(\delta\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Photo \(\rightarrow\) Map \\ _Google Maps_ \\ \end{tabular} } & \multirow{2}{*}{PRM} & 25 & **18.8** & 17.5 & 18.0 & 18.1 & 18.4 & 5.1 \\  & & 50 & **19.8** & 18.7 & 19.3 & 19.2 & 19.5 & 4.3 \\  & & 100 & **21.7** & 20.4 & 20.8 & 20.7 & 21.1 & 3.4 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Photo \(\rightarrow\) Semantic label \\ _CityScapes_ \\ \end{tabular} } & FCN & 25 & **0.57** & 0.43 & 0.45 & 0.46 & 0.50 & 16.3 \\  & 50 & **0.63** & 0.49 & 0.52 & 0.51 & 0.54 & 10.2 \\  & 100 & **0.71** & 0.52 & 0.56 & 0.55 & 0.58 & 11.5 \\ \cline{2-8}  & \multirow{2}{*}{\
\begin{tabular}{c} FCN \\ mean acc. \\ \end{tabular} } & 25 & 0.16 & 0.13 & 0.15 & 0.16 & **0.18** & 38.5 \\  & 50 & **0.20** & 0.15 & 0.17 & 0.18 & 0.19 & 26.7 \\  & 100 & **0.25** & 0.17 & 0.19 & 0.20 & 0.22 & 29.4 \\ \cline{2-8}  & \multirow{2}{*}{
\begin{tabular}{c} FCN \\ mean IoU \\ \end{tabular} } & 25 & **0.13** & 0.09 & 0.10 & 0.10 & 0.12 & 33.3 \\  & 50 & **0.16** & 0.10 & 0.11 & 0.12 & 0.13 & 30.0 \\  & 100 & **0.18** & 0.11 & 0.13 & 0.13 & 0.15 & 36.4 \\ \hline \hline \end{tabular} Note: all values are expressed in [%]; higher PRM is better (% of gen. images misinterpreted as real); maximum for each \(S_{X}\) is typeset in bold and the largest \(\delta\) over all \(S_{X}\) is shaded in green.  
\end{table}
Table 2: Results of the second type of experiments. Performance of the proposed SSL+BTS was compared against unsupervised CycleGAN, and supervised pix2 pix by using Perceptual Realistic Measure (PRM) for the ’Photo \(\rightarrow\) Map’ domain translation task, and by assessing image segmentation quality in the case of ’Photo \(\rightarrow\) Semantic label’ task. Relative improvement of SSL+BTS over CycleGAN is expressed by \(\delta\), while the respective datasets are listed in the table.  
shown for _Google Maps_, _CityScapes_ and _Facade_ datasets. It can be seen that in those examples, the proposed BTS-SSL CycleGAN obtains visually more accurate results than the baseline methods.  
Figure 4: Visual examples of the proposed vs. baseline algorithm comparisons for 25% of the scarce domain data used: Real A (scarce domain) and Real B (target domain) correspond to image pair examples. Examples are given for _Google Maps_, _CityScapes_ and _Facade_ datasets. SSL denotes that only SSL on the control part of pared images is included in the training of CycleGAN, BTS denotes that only bootstrapping of the statistic of the A discriminator pool is included in the training, while SSL+BTS denotes that both SSL and BTS are included.  
Figure 5: Visual examples of the proposed vs. baseline algorithm comparisons for 50% of the scarce domain data used: Real A (scarce domain) and Real B (target domain) correspond to image pair examples. Examples are given for _Google Maps_, _CityScapes_ and _Facade_ datasets. SSL denotes that only SSL on the control part of pared images is included in the training of CycleGAN, BTS denotes that only bootstrapping of the statistic of the A discriminator pool is included in the training, while SSL+BTS denotes that both SSL and BTS are included.