## 4 Experimental Evaluation of the GAGAN Model
### Numerical Analysis
The g_loss column reflects the generator's ability to produce realistic images that successfully deceive the discriminator, while the d_loss column shows the discriminator's effectiveness in distinguishing real images from generated ones. Lower values in these columns suggest improved model performance, with balanced losses indicating stable adversarial training. To further assess model performance, the means and variances shown in Table 3 are derived from the data provided in Table 2. The computed statistic metrics for the DCGAN and GAGAN models provide a detailed evaluation of their performance across multiple training runs. For DCGAN, the discriminator loss has a mean value of 0.410, indicating its average performance, and a variance of 0.0121, reflecting relatively consistent behavior. The generator loss for DCGAN has a mean of 2.5420, with a variance of 0.1252, showing slightly greater variability in how well the generator adapts during training. In contrast, GAGAN's discriminator loss exhibits a significantly lower mean of 0.0690, coupled with a minimal variance of 0.00025, underscoring its stability and robust performance across runs. Meanwhile, the generator loss for GAGAN has a higher mean of 7.110 and a variance of 1.5986, indicating greater fluctuation in its adaptation process but demonstrating its overall effectiveness in generating diverse outputs. These metrics are further illustrated in Figure 8, which displays the mean and variance of the generator and discriminator losses for both GAGAN and DCGAN, highlighting the stability of the discriminator in GAGAN and the more pronounced fluctuations in its generator loss across multiple training runs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model/Training Run No.** & **g\_loss** & **d\_loss** \\ \hline DCGAN/1 & 3.0347 & 0.4647 \\ DCGAN/2 & 2.5461 & 0.2854 \\ DCGAN/3 & 2.1455 & 0.3585 \\ DCGAN/4 & 2.8245 & 0.3433 \\ DCGAN/5 & 2.1596 & 0.5967 \\ GAGAN/1 & 6.4968 & 0.0676 \\ GAGAN/2 & 8.0332 & 0.0786 \\ GAGAN/3 & 7.6608 & 0.0635 \\ GAGAN/4 & 7.6245 & 0.0693 \\ GAGAN/5 & 5.8762 & 0.0665 \\ GAGAN/6 & 8.6764 & 0.0273 \\ GAGAN/7 & 5.6197 & 0.0852 \\ GAGAN/8 & 8.1790 & 0.0704 \\ GAGAN/9 & 4.7299 & 0.0855 \\ GAGAN/10 & 8.1988 & 0.0761 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Loss comparison between DCGAN and GAGAN models. The comparative analysis reveals that GAGAN models consistently outperform DCGAN models in terms of discriminator loss (d_loss), indicating a more effective training of the discriminator. For instance, GAGAN/1 achieved a d_loss of 0.0676, which is significantly lower than DCGAN/1's d_loss of 0.4647. Similarly, GAGAN/6 and GAGAN/3 displayed strong performance, with d_loss values of 0.0273 and 0.0635, respectively, highlighting their superior ability to distinguish real from generated images. The near-zero d_loss values observed in GAGAN/6 and GAGAN/3 suggest a highly proficient discriminator, capable of accurately identifying real data. In contrast, DCGAN models exhibited consistently higher d_loss values, with DCGAN/5 reaching a peak of 0.5967, indicating a less effective discriminator in discerning between authentic and synthetic images. In terms of generator loss (g_loss), GAGAN models showed varied performance, with some models excelling and others encountering challenges. For example, GAGAN/5 and GAGAN/9 achieved lower g_loss values of 5.8762 and 4.7299, respectively, indicating that these generators were more successful in producing images that deceived the discriminator effectively. However, higher g_loss values were observed in GAGAN/6 (8.6764) and GAGAN/8 (8.1790), reflecting occasional difficulties in generating convincing images. Interestingly, despite these higher g_loss values, the corresponding low d_loss values suggest that the discriminators in these cases were well-trained and presented a greater challenge for the generators to overcome.