## 3 Results and Discussion
The IS value with this combination (2.43) may have influenced this result, promoting more significant variability in artificial images, and consequently, better model generalization. Considering the RAGAN-based models, it is possible to note that XRAGAN + DeepLIFT provided the lowest FID (46.02) and the highest accuracy in most cases, 84.30% with ViT and 91.92% with PVT. Also, XWGAN-GP + Saliency provided the lowest FID (72.01  
accuracy with ViT (84.41%), DEiT (88.09%), and CoAtNet (93.64%). The XWGAN-GP + Gradient\(\odot\)Input provided the second-lowest FID (74.01) and achieved the highest accuracy with PVT (92.13%). Regarding the LA dataset, XDCGAN could not improve the FID (Table 1). XDCGAN + Saliency, however, achieved the second-lowest FID (128.07), and it is worth noting in Table 3 that this combination provided the best accuracy with DEiT (95.45%). It is also worth noting that the architecture with no GAN improved the classification performance with ViT, which achieved 95.04% without data augmentation. Nevertheless, taking into account the RAGAN-based architectures, the combination with the best FID, the XRAGAN + DeepLIFT, provided the best classification performance with PVT, 97.71% against 97.32%  
Figure 9: Examples of generated images for the LG dataset. with RAGAN, and 96.44% without data augmentation. XWGAN-GP improved DEiT, PVT, and CoAtNet classification performance compared to WGAN-GP. For instance, WGAN-GP + Saliency achieved an accuracy of 97.79% with DEiT and 98.24% with PVT. This result represents an improvement of 3.81% and 1.87% compared to the models without data augmentation, respectively, and 3.01% and 1.23% compared to WGAN-GP. Also, the combination XWGAN-GP + DeepLIFT, which provided the second-best FID and IS, achieved the best accuracy with CoAtNet, 99.34%. Figure 10: Examples of generated images for the UCSB dataset. Table 4 shows the accuracy of the LG dataset. Once again, no architecture was able to improve classification with ViT. However, the two best XDCGAN models in terms of FID (Table 1), XDCGAN + Gradient\(\odot\)Input (99.33) and XDCGAN + DeepLIFT (98.24), achieved the highest accuracy with PVT (99.20%) and DEiT (97.43%), respectively. The XRAGAN model showed improvements in FID and IS when combined with Saliency (Table 1). This combination achieved the highest accuracy with DEiT (96.77%) and PVT (99.08%). Also, the combination XRAGAN + Gradient\(\odot\)Input achieved 99.79% with CoAtNet. The XWGAN-GP model also improved FID and IS when combined with Saliency. It achieved the highest accuracy with DEiT (97.17%) and CoAtNet (99.81%). The XWGAN-GP + Gradient\(\odot\)Input combination provided the second-lowest FID and achieved the highest accuracy with PVT (98.94%). Table 5 shows the accuracy of the UCSB dataset. The XDCGAN + Gradient\(\odot\)Input achieved the best results with DEiT (75.14%) and PVT (78.36%). Regarding the XRAGAN models, XRAGAN + Saliency and XRAGAN + Gradient\(\odot\)Input achieved the lowest FID (Table 1). XRAGAN + Saliency achieved the highest accuracy with DEiT (75.88%) and XRAGAN + Gradient\(\odot\)Input with PVT (77.36%). The XWGAN-GP + Gradient\(\odot\)Input combination provided the lowest FID (87.65) and achieved the highest accuracy with DEiT (77.42%), PVT (77.77%), and CoAtNet (76.59%).