# Use of Generative Adversarial Networks (GAN) for
## 3 Results
### Phase 2: GAN-Augmented Sampling and Model Testing  
The GAN-augmented data yielded slightly lower accuracy rates for the testing sets than the raw data (Table 3). It produced similar estimates to previous testing using high-resolution images [1]. In contrast, it also yielded higher balanced accuracy estimates (Table 3). The greater increase in F1-scores is the most modified outcome. In this case, the results are highly variable depending on the combination and not a clear strategy is visible. With \"SGD\", \"swish\" produced better results with some models but not others, and the same is observed for \"Adam\" and \"relu\". Overall, VGG16 and ResNet50 were the best models. In contrast, the Jason2 model was less successful and its accuracy stagnated several points below, ranging from 57.1 to 88.9 (Table 3). It also yielded lower estimates of balanced accuracy; however, it still outperforms its efficiency when using the non-augmented dataset, by having improved the balanced accuracy by almost ten points in some of the combinations [1]. Within each model, the activation function plays a minor  
Figure 5: Accuracy and loss values for the VGG16 and ResNet50 models on the GAN-augmented dataset displaying two different combinations of activation function and optimizer: swish-SGD (VGG16) and swish-SGD (ResNet50). Horizontal axis displays the number of epochs. role in the outcome, and the optimizer seems more relevant, but less so than with the raw dataset. It is the model architecture that makes a difference. The Bayesian networks supported these interpretations. Several algorithms provide different insights into the variable relationship network. The \"BIC\" (score = \(-217.1542\)), the \"loglik\" (score = \(-76.27329\)), the \"bde\" (score = \(-202.0743\)), and the \"bbla\" (score = \(-203.2623\)) hill-climbing score factors indicated different options. The \"loglik\" factor suggests that the model architecture has the major impact on all the other elements (Figure 6); however, this model has the largest AIC (Akaike information criterion) score. The \"bde\" score factor suggests a more complex picture, in which the parameters affect the model performance. This is more specifically nuanced by the \"bbla\" factor, which indicates that although the model architecture has a major impact on the performance of the activation function and the resulting balanced accuracy, it is the optimizer that has a major role in model performance (Figure 6). If we select the optimal network, resulting from the BIC score factor, it can be concluded that all these relationships are of interest, but in general, the only meaningful relationship is that between the accuracy and its impact on loss and the F1-score, underscoring that with the GAN-augmented dataset, the activation function and the optimizer do not create significant differences within each model architecture performance. It could be argued, based on these results, that the augmented dataset contributed to building a more reliable model, because even if the general accuracy did not improve or became slightly lower, the balanced accuracy was significantly higher.