# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 4 Experimental Results
### Discussion  
In this section, we present the effectiveness of domain adaptation based on the proposed bidirectional feature learning method with generative networks through experimental results. We also describe our main contributions through comparison with existing domain adaptation techniques. The quantitative evaluation showed that the bidirectional feature learning method had higher accuracy and stability than the one-way feature conversion method. Domain adaptation results between various datasets confirmed that the bidirectional feature learning with generative network outperformed previous studies such as DANN and ADDA. Although it might not be possible to conclude that the robustness of the model was improved based on the experimental results alone, considering the flow of minimizing domain information, increasing similarity between features, and reducing gaps from one-way learning to two-way learning, it is clear that bidirectional feature learning with a generative network in the field of domain adaptation is effective because the accuracy and various evaluation metrics recorded higher values than for previous domain adaptation studies. The visual quality evaluation visually confirmed that the bidirectional feature learning method produced good results in domain adaptation. This was determined to be a result of the generative network more accurately reflecting the actual distribution of the data. The effect of the bidirectional feature transformation strengthened the interdependence \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**EMNIST \(\rightarrow\) MNIST**} \\ \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline
**Source only** & 0.756 & 0.621 & 0.630 \\ \hline
**DANN** & 0.821 & 0.832 & 0.831 \\ \hline
**ADDA** & 0.895 & 0.894 & 0.894 \\ \hline
**BiFLP-AdvDA** & **0.998** & **0.996** & **0.996** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Adversarial domain adaptation for EMNIST (target domain) to MNIST (source domain) dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**SVHN \(\rightarrow\) MNIST**} \\ \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline
**Source only** & 0.531 & 0.528 & 0.531 \\ \hline
**DANN** & 0.676 & 0.673 & 0.665 \\ \hline
**ADDA** & 0.726 & 0.711 & 0.703 \\ \hline
**BiFLP-AdvDA** & **0.988** & **0.988** & **0.988** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Adversarial domain adaptation for SVHN (target domain) to MNIST (source domain) dataset. between the two domains. Taken together, this study clearly demonstrates that the combination of the bidirectional feature learning method and generative network shows higher performance than various other domain adaptation techniques. It is an effective method for reducing distribution difference between datasets and improving the adaptive ability of the model. The limitations of this study are discussed in this section. We believe that there are four main limitations of this paper. First, this paper proposed a method to solve the domain adaptation problem by reconstructing real image data, but when the variability in the domain is high, the quality and generalization ability of the reconstructed image data may be limited. In particular, it is necessary to discuss how to overcome the limitations for the adaptation problem between complex and diverse domains. Second, the proposed method relies on the amount of training data. In the absence of a sufficient amount of labeled data, the generalization ability of the model may suffer. This is a common problem in real-world scenarios, and we need to discuss how to develop effective models even with small amounts of data. Third, although the reconstructed image data were considered as noisy image data, the performance of the model may fluctuate depending on the degree and shape of the noise. This may cause the model to be sensitive to noise, and ways to improve this instability are needed.