# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 5 Experimental Results
### Quantitative Evaluation of the Conducted Experiments  
Results of the conducted experiments are summarized in Tables 1 and 2. The relative improvement brought by the proposed method in Table 1 is expressed by average \(\delta_{\text{PSNR}}\) and \(\delta_{\text{SSIM}}\), while SSL and BTS denote results of the methods corresponding to individual elements of the proposed SSL-BTS learning strategy. BTS training includes only bootstrapping of the discriminator \(D_{X}\), without paired samples, while SSL involves using paired samples, but without bootstrapping. As an example of the performance of supervised methods on the same tasks, results of pix2 pix method are also presented. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \(\mathcal{S}_{X}\) & **pix2 pix** & **CycleGAN** & \multicolumn{2}{c}{**SSL**} & \multicolumn{2}{c}{**BTS**} & \multicolumn{2}{c}{**SSL+BTS**} & \(\delta_{\text{PSNR}}\) & \(\delta_{\text{SSIM}}\) \\ \cline{2-13}  & **[\%]** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **[\%]** & **[\%]** \\ \hline \multirow{3}{*}{_CitySapes_} & 25 & 19.98 & 0.60 & 17.20 & 0.56 & 18.30 & 0.59 & 17.30 & 0.58 & 18.77 & 0.61 & 9.1 & 8.9 \\  & 50 & 20.45 & 0.64 & 17.00 & 0.55 & 18.95 & 0.61 & 17.18 & 0.59 & 19.04 & 0.64 & 12.00 & 16.4 \\  & 100 & 19.51 & 0.59 & 17.12 & 0.54 & 20.03 & 0.58 & 17.75 & 0.63 & **20.47** & 0.65 & 19.6 & 20.4 \\ \hline \multirow{3}{*}{_Facade dataset_} & 25 & **13.78** & 0.35 & 10.93 & 0.25 & 11.78 & 0.31 & 10.81 & 0.27 & 11.83 & 0.32 & 8.23 & 28.0 \\  & 50 & **14.24** & 0.40 & 11.00 & 0.25 & 13.22 & 0.37 & 11.92 & 0.28 & 13.75 & 0.40 & 25.0 & 60.0 \\  & 100 & **14.25** & 0.42 & 10.98 & 0.27 & 12.88 & 0.33 & 11.52 & 0.35 & 13.21 & 0.41 & 20.3 & 51.8 \\ \hline \multirow{3}{*}{_Google Maps_} & 25 & 30.35 & 0.67 & 30.47 & 0.71 & 30.62 & 0.73 & 30.55 & 0.75 & **31.20** & 0.77 & 2.4 & 8.4 \\  & 50 & 30.55 & 0.68 & 29.78 & 0.72 & 30.68 & 0.75 & 29.81 & 0.76 & **30.88** & 0.79 & 3.7 & 9.7 \\ \cline{1-1}  & 100 & 30.01 & 0.69 & 30.24 & 0.73 & 30.92 & 0.75 & 30.27 & 0.77 & **31.23** & 0.81 & 3.3 & 11.0 \\ \hline \hline \end{tabular} Note: maximum for each \(\mathcal{S}_{X}\) is typeset in bold (PSNR) and blue bold with blue highlight (SSIM), while the largest \(\delta\) over all \(\mathcal{S}_{X}\) is shaded in green.  
\end{table} 
Table 1: Experimental comparison between proposed semi-supervised (SSL+BTS) and the unsupervised CycleGAN, under different scenarios: varying sample size \(\mathcal{S}_{X}\) of scarce domain \(\mathcal{X}\), as well as when applied to different tasks/datasets—_CitySapes_, _Facade dataset_, _Google Maps_. In Table 1 the results of the PSNR, as well as SSIM measure between images generated by the considered GAN algorithms (baseline pix2 pix, CycleGAN as well as the proposed BTS-SSL CycleGAN, in an image-to-image translation task, from scarce to target domain) and ground truth images, are presented, for all the dataset used. From Table 1 it can be seen that the proposed BTS-SSL CycleGAN algorithm obtains better results on average, by both PSNR and SSIM measures, in all of the experiments, in comparison to the CycleGAN method, especially on _CityScapes_ and CMP _Facade_ datasets. It can also be seen that both SSL and BTS components of the proposed algorithm contribute significantly to the obtained results. In Table 2, the PRM results for the _Google Maps_ dataset and the Photo\(\rightarrow\) Map task, for the proposed method in comparison to the baseline methods, are given in form of the accuracy of detecting generated, i.e., false images by the participants, and expressed in %. It can be seen that, similarly to other experiments, the proposed BTS-SSL CycleGAN outperforms the baseline CycleGAN, but as expected, does not outperform the fully supervised pix2 pix method. Furthermore, both BTS as well as SSL components of the method contribute to the final result.