## 3 Results and Discussion  
We used XAI explanations to improve the training of GANs and generate artificial images with higher quality and variability. Figures 7-10 show some examples of the original images and those generated by the GAN and XGAN models for the CR, LA, LG, and UCSB datasets, respectively. We conducted experiments using the FID and IS metrics to assess quantitatively the quality and variability of the artificial images. Table 1 shows the results of these experiments. The results are organized by base architecture: DCGAN, RAGAN, and WGAN-GP. Scores in bold indicate the best results regarding the base architecture. The green and red arrows indicate whether the FID and IS obtained with XGAN are better or worse than those obtained with the original architecture. Figure 6: Schematic illustration of the classification evaluation process. Considering the CR dataset (Figure 7), it is possible to note in Table 1 that XDCGAN + Saliency achieved the best FID and IS regarding the DCGAN-based architectures. The values were 57.01 and 2.43, respectively. Considering the RAGAN-based models, it is worth noting that all combinations of XRAGAN improved the FID and IS compared with RAGAN. The XRAGAN + DeepLIFT was the highlight, providing the lowest FID, 46.02, representing a 32.70% decrease compared to RAGAN (68.39). The XWGAN-GP also improved the FID in all cases. The combination XWGAN-GP + Saliency provided the lowest FID, 72.01, 13.04% lower than WGAN-GP (82.81). On the LA dataset (Figure 8), XDCGAN did not improve the FID and IS. DCGAN achieved the best quality, with an FID of 127.94 and an IS of 1.57. However, considering RAGAN-based models, XRAGAN + DeepLIFT and XRAGAN + Gradient\(\odot\)Input improved the FID and IS slightly. The best combination was XRAGAN + DeepLIFT, which provided an FID improvement of 2.8%, 104.08 against 107.15 with RAGAN. On the other hand, XWGAN-GP improved the FID in all cases. XWGAN-GP + Saliency provided the best FID, 152.19, representing a 20.56% decrease compared to WGAN-GP. Regarding the LG dataset (Figure 9) and the performance using DCGAN-based architectures (Table 1), XDCGAN + Gradient\(\odot\)Input achieved the best FID, 93.88, representing a 13.22% decrease compared to DCGAN (108.18). Considering RAGAN-based models, XRAGAN + Saliency and XRAGAN + Gradient\(\odot\)Input improved the FID and IS. XRAGAN + Saliency provided the best FID, 88.30, representing an 8.99% decrease compared to RAGAN (95.91). When using WGAN-GP as the base architecture, XWGAN-GP + Saliency provided the best FID, 128.26, 6.52% less than WGAN-GP (137.46). Finally, considering the UCSB dataset (Figure 10), XDCGAN + DeepLIFT achieved an FID of 62.34 and an IS of 2.83. This combination was the best FID and IS among the DCGAN-based architectures, representing about a 14.33% FID improvement. Considering the RAGAN-based models, XRAGAN + Saliency and XRAGAN + Gradient\(\odot\)Input improved the FID and IS. XRAGAN + Saliency provided the best FID, 63.21, representing a 7.61% decrease compared to RAGAN (68.42). This combination also produced more diverse images. The IS score was 2.79. XWGAN-GP showed no improvement compared to WGAN-GP. The best case was WGAN-GP, with an FID of 81.07. However, XWGAN-GP provided the best IS, 2.74. We used the generated images to augment the datasets and train the Transformer models. Our goal was to verify whether the XAI methods' feature information could improve the Transformer models' classification performance. We followed the strategy described in Section 2.4.2. Tables 2-5 show the classification results on the CR, LA,