# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 5 Discussion  
We introduced an image-to-image translation method that uses a pre-trained auto-encoder and decoder to enhance the quality of translation. First, we trained two distinct auto-encoders for the source domain X and target domain Y, respectively. Then, we cross-connected the pre-trained auto-encoder (for the feature extractor of X) and auto-decoder (for the reconstructor of Y), adding a feature mapping layer. In experiments, we showed that our method improves the quality of translation results significantly in comparison with the existing baselines. We also investigated how to add a feature mapping layer in order to further enhance the performance of our model. Despite qualitative and quantitative improvements identified in experiments, our method is limited to unimodal image-to-image translation. We plan to investigate more sophisticated feature mapping architectures between the auto-encoder and decoder by conducting experiments on multimodal tasks.  
Conceptualization and methodology, J.Y., H.E., and Y.S.C.; investigation, J.Y. and H.E.; data curation and conceiving experiments, J.Y.; performing experiments and designing results, H.E.; writing--original draft preparation, J.Y. and H.E.; writing--review and editing, J.Y., H.E., and Y.S.C.  
This work was supported by the National Research Foundation of Korea (NRF), a grant funded by the Korean government (Ministry of Science and ICT) (2018R1A5A7059549), the ITRC (Information Technology Research Center) support program (IITP-2017-0-01642) supervised by the IITP (Institute for Information and communications Technology Promotion, Korea), and the Technology Innovation Program (10077553) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & \multicolumn{6}{c}{**Photo \(\rightarrow\) Label**} \\  & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **MAE \(\downarrow\)** & **MSE \(\downarrow\)** & **IS \(\uparrow\)** & **FID \(\downarrow\)** \\ \hline Baseline (CycleGAN) & **0.5568** & 15.0977 & **25.7486** & 2185.6808 & 2.1170 \(\pm\) 0.1927 & 107.4895 \\ Ours w/o feature mapping layer & 0.5054 & 13.5550 & 37.9918 & 2999.5845 & **2.2096 \(\pm\) 0.1290** & 107.3393 \\ Ours + \(1\times 1\) convolutional layer & 0.5326 & 14.5956 & 33.2103 & 2439.4565 & 1.9608 \(\pm\) 0.1521 & 96.7853 \\ Ours + \(3\times 3\) convolutional layer & 0.5526 & **15.4477** & 29.9302 & **2040.3728** & 2.0509 \(\pm\) 0.1997 & **93.4251** \\ Ours + \(7\times 7\) & & & & & & \\ depth-wise convolutional layer & 0.5029 & 13.1269 & 37.9808 & 3301.7309 & 2.1264 \(\pm\) 0.1557 & 126.5632 \\ Ours + \(1\times 1\) convolutional layer + residual connection & 0.5006 & 13.6503 & 39.0486 & 2928.5135 & 2.1674 \(\pm\) 0.1042 & 120.7020 \\ (in FML) & & & & & & \\ Ours + \(3\times 3\) convolutional layer + residual connection & 0.5017 & 13.5443 & 37.6828 & 3026.7782 & 2.0445 \(\pm\) 0.1418 & 108.9408 \\ (in FML) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of our model on cityscapes dataset (photo \(\rightarrow\) label). Higher value is better in SSIM, PSNR, IS. Lower value is better in MAE, MSE, FID.