# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 4 Experimental Results
### Results  
In this section, we will describe experiments conducted using a bidirectional-feature-learning-based domain adaptation method with a generative network. We will also provide the overall experimental environment and experimental results. As a quantitative evaluation, we have tried to prove the validity of this paper by comparing the proposed model with the DANN and ADDA models that already exist. To prove how well the domain adaptation is achieved, in addition to the comparison between models, the proposed model was trained only in the source domain and the test accuracy was measured and compared. This shows that it is an effective way to realize better model adaptation. By using the loss function, which was not included in the models implemented in the previous studies mentioned above, the interaction between the generative network and the feature conversion process could reduce the difference in the distribution of data, increase the similarity of the extracted features, and strengthen the interdependence between the two domains. This approach was successfully implemented through numerical comparison of several evaluation indicators. As a qualitative evaluation, the comparison of data points in the source and target domains before and after learning provided a visualization of how well the domain adaptation was achieved. This suggests that bidirectional feature learning can effectively reduce distributional differences between datasets, significantly reducing the distance between unadapted and adapted data. Figure 7 shows a visualization of data points without adaptation and with adaptation. It could be seen from actual experiments that the two-way feature-learning-based domain adaptation method with a generative network outperformed the traditional one-way feature conversion method. Compared to traditional domain adaptation techniques, the method proposed in this paper showed higher accuracy and stability in quantitative evaluation. Each row shows the results of experiments conducted on different datasets divided into source and target domains, with red indicating the target domain and blue indicating the source domain. From the top, each row was organized in the following order: MNIST \(\rightarrow\) USPS, SVHN \(\rightarrow\) MNIST, EMNIST \(\rightarrow\) MNIST. In addition, each column could be divided into cases without domain adaptation and with domain adaptation in each experimental environment. From the left, column 1 represents the case without domain adaptation and column 2 represents the case with domain adaptation. It can be seen from Figure 7 that the model proposed in this study performed well in domain adaptation. The domain adaptation results in this paper are shown in Table 3. We can see that the proposed method outperformed other methods by a large margin, except for the SVHN to MNIST experimental results. This figure shows the test accuracy of the experiments, which is a value between 0 and 1. The maximum value of 1 indicates 100% accuracy. Therefore, the results of this model showed a high accuracy, of more than 90%, except for the one case mentioned above. Even the comparison of figures with existing studies and the results of learning and testing only the source domain showed a lot of differences. This indicates that the two-way approach of feature transformation and feature learning through generative networks can improve the generalization ability of the model by more effectively reducing distributional differences between domains. Furthermore, Tables 4-6 show that the ADDA paper is part of a follow-up study published after the DANN paper. It does not change the fact that it performs better than DANN to some extent. However, the moment when the DANN model has a higher accuracy value than the ADDA model is the EMNIST to MNIST experiment shown in Table 3. On the other hand, the proposed model has a value of more than 0.98 for evaluation metrics, with the highest value close to 0.998, which means that the performance is good.