## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & **CR** & & **LA** & & **LG** & & **UCSB** & \\ \hline \multirow{8}{*}{DCGAN} & & **FID** & **IS** & **FID** & **IS** & **FID** & **IS** & **FID** & **IS** \\ \cline{2-10}  & No XAI & 59.13 & 2.41 & **127.94** & **1.57** & 108.18 & 1.40 & 72.77 & 2.78 \\  & Saliency & **57.01**\(\downarrow\) & **2.43**\(\uparrow\) & 128.07 \(\uparrow\) & 1.48 \(\downarrow\) & 114.01 \(\uparrow\) & 1.38 \(\downarrow\) & 70.72 \(\downarrow\) & 2.72 \(\downarrow\) \\  & DeepLIFT & 62.45 \(\uparrow\) & 2.38 \(\downarrow\) & 134.21 \(\uparrow\) & 1.46 \(\downarrow\) & 98.24 \(\downarrow\) & **1.42**\(\uparrow\) & **62.34**\(\downarrow\) & **2.83**\(\uparrow\) \\  & Gradient\(\odot\)Input & 60.32 \(\uparrow\) & **2.43**\(\uparrow\) & 133.88 \(\uparrow\) & 1.51 \(\downarrow\) & **93.88**\(\downarrow\) & 1.39 \(\downarrow\) & 69.90 \(\downarrow\) & 2.72 \(\downarrow\) \\ \hline \multirow{4}{*}{RAGAN} & No XAI & 68.39 & 2.40 & 107.15 & 1.52 & 95.91 & **1.45** & 68.42 & 2.62 \\  & Saliency & 50.39 \(\downarrow\) & 2.45 \(\uparrow\) & 124.82 \(\uparrow\) & 1.58 \(\uparrow\) & **88.30**\(\downarrow\) & 1.42 \(\downarrow\) & **63.21**\(\downarrow\) & **2.79**\(\uparrow\) \\  & DeepLIFT & **46.02**\(\downarrow\) & 2.50 \(\uparrow\) & **104.08**\(\downarrow\) & 1.50 \(\downarrow\) & 101.78 \(\uparrow\) & 1.40 \(\downarrow\) & 69.73 \(\uparrow\) & 2.62 \(\uparrow\) \\  & Gradient\(\odot\)Input & 58.57 \(\downarrow\) & **2.56**\(\uparrow\) & 105.83 \(\downarrow\) & **1.61**\(\uparrow\) & 95.12 \(\downarrow\) & 1.40 \(\downarrow\) & 66.68 \(\downarrow\) & 2.65 \(\uparrow\) \\ \hline \multirow{4}{*}{WGAN-GP} & No XAI & 82.81 & 2.21 & 191.59 & 1.51 & 137.46 & 1.45 & **81.07** & 2.44 \\  & Saliency & **72.01**\(\downarrow\) & 2.28 \(\uparrow\) & **152.19**\(\uparrow\) & **1.59**\(\uparrow\) & **128.26**\(\downarrow\) & 1,43 \(\downarrow\) & 92.27 \(\uparrow\) & 2.50 \(\uparrow\) \\ \cline{1-1}  & DeepLIFT & 76.86 \(\downarrow\) & **2.29**\(\uparrow\) & 158.51 \(\downarrow\) & 1.56 \(\uparrow\) & 153.10 \(\uparrow\) & 1,57 \(\uparrow\) & 89.34 \(\uparrow\) & 2.55 \(\uparrow\) \\ \cline{1-1}  & Gradient\(\odot\)Input & 74.50 \(\downarrow\) & 2.17 \(\downarrow\) & 178.23 \(\downarrow\) & 1.35 \(\downarrow\) & 143.97 \(\uparrow\) & 1,50 \(\uparrow\) & 87.65 \(\uparrow\) & **2.74**\(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FID and IS scores for CR, LA, LG, and UCSB datasets. Scores in bold indicate the best results regarding the base architecture, with arrows indicating whether XGAN performs better (green arrow) or worse (red arrow) than the original architecture. LG, and UCSB datasets, respectively. Each table's first row presents the classification accuracy without data augmentation (without DA), and results using a GAN in the data augmentation are organized by base architecture: DCGAN, RAGAN, and WGAN-GP. We considered the classification performance without data augmentation and with data augmentation using original architectures (DCGAN, RAGAN, and WGAN-GP with no XAI) as a baseline. We compared the results obtained with XGAN (XDCGAN, XRAGAN, and XWGAN-GP) with the defined baselines to determine whether the proposed method is capable of improving the classification performance of Transformer-based models via data augmentation and whether educational training using XAI is capable of enhancing the quality and classification performance of a given base GAN architecture. Thus, bold values indicate the best accuracy given a base architecture. Figure 7: Examples of generated images for the CR dataset. Considering the classification results regarding the CR dataset (Table 2), the combination with the best FID among the DCGAN-based architectures, XDCGAN + Saliency, also provided the best classification performance. This combination provided the best accuracy with all Transformer models: 84.14% with ViT, 87.70% with DEiT, 92.24% with PVT, and 93.44% with CoAtNet. With CoAtNet, XDCGAN + Gradient\(\odot\)Input also achieved an accuracy of 93.44%, the same as XDCGAN + Saliency.