# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 4 Experiment Results and Evaluation
### Facial Expression Recognition
We visualize the feature distribution of the two domains before and after the adaptation (J\(\rightarrow\)C), as shown in Figure 5. Figure 5 proves that our model can make the distribution of the features from the two domains much closer, which brings about a higher accuracy in the classification. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Method**} & \multicolumn{2}{c}{**Decoupled Layers**} & \multirow{2}{*}{**J\(\rightarrow\)M**} & \multirow{2}{*}{**M\(\rightarrow\)J**} & \multirow{2}{*}{**C\(\rightarrow\)M**} & \multirow{2}{*}{**M\(\rightarrow\)C**} & \multirow{2}{*}{**J\(\rightarrow\)C**} & \multirow{2}{*}{**C\(\rightarrow\)J**} \\  & **ENC** & & & & & & & & \\ \hline Source & & & & 0.330 & 0.362 & 0.428 & 0.697 & 0.634 & 0.437 \\ UNIT & \(-\) & \(-\) & \(-\) & 0.507 & 0.470 & 0.567 & 0.719 & 0.733 & 0.526 \\ CGAA & \(\surd\) & \(\surd\) & \(-\) & **0.521** & 0.460 & **0.581** & **0.736** & 0.744 & 0.559 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **0.521** & **0.498** & **0.581** & **0.736** & **0.769** & **0.573** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition accuracy evaluation for domain adaptation on facial expression datasets. JAFFE(J), MMI(M), CK+(C). J\(\rightarrow\)M means J is the source and M is the target. Bold numbers are the best results. Figure 4: The style-transferred images generated by our model when trained to adapt CK+ to JAFFE. (**a**) The Source images from CK+. (**b**) The generated samples when adapt CK+ images in (**a**) into JAFFE style using UNIT. (**c**) The generated samples when adapt CK+ images in (**a**) into JAFFE style using our method CGAA. (**d**) Random target images from the JAFFE. To further evaluate the effectiveness of our method, we compare the confusion matrices of the class-wise classification accuracy on target domain before and after adaptation. As shown in Figure 6, the blue ones are the confusion matrices when only source domain images are used for training, the green ones are matrices when UNIT is used for domain adaption and the red ones are matrices when our method is used for domain adaptation. When trained on source domain only, the model have difficulties separating _Angry_ and _Neutral_ between CK+ and JAFFE (see Figure 6a,b, and also cannot seperate _Angry_ and _Sad_ between MMI and CK+ (see Figure 6c,d). When trained on MMI and tested on JAFFE, the model misclassifies a lot of images as _Surprised_ (see Figure 6e) whereas when trained on JAFFE and tested on MMI, the model misclassifies most of the images as _Angry_ (see Figure 6f). These misclassifications are caused by the semantic gap between domains. Figure 6 shows that our domain adaptation method can help the model to cross the semantic gap between domains and increase the class-wise classification accuracies. Figure 5: The effect of our adaptation depicted by t-SNE [40] visualizations of the extracted features from the last hidden layer of the classifier in the JAFFE to CK+ scenario. Blue dots are examples from the source dataset JAFFE, red dots are examples from the target dataset CK+. (**a**) is when only the source dataset is used for training. (**b**) is when our adaptation procedure is done. The adaptation of our method makes the distribution of the features from the two datasets much closer.