## 4 Experimental Evaluation of the GAGAN Model
### Numerical Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model/Training Run No.** & **FID** & **IS Mean** & **IS STD** \\ \hline DCGAN/1 & 295.6075 & 2.5592 & 0.2140 \\ DCGAN/2 & 295.3973 & 2.7832 & 0.1698 \\ DCGAN/3 & 293.9751 & 3.0798 & 0.2913 \\ DCGAN/4 & 308.9873 & 2.6243 & 0.3303 \\ DCGAN/5 & 298.0760 & 3.4491 & 0.2243 \\ GAGAN/1 & 120.4281 & 1.9632 & 0.1701 \\ GAGAN/2 & 115.2125 & 2.032 & 0.1473 \\ GAGAN/3 & 112.4268 & 2.0292 & 0.1986 \\ GAGAN/4 & 113.0503 & 2.0144 & 0.2139 \\ GAGAN/5 & 114.7395 & 2.0652 & 0.2098 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FID and IS Metrics for GAGAN and DCGAN Models. shows GAGAN achieving scores between 112.4 and 120.4. These values are substantially better compared to DCGAN's range of 293.9 to 308.9, underscoring GAGAN's superior capability in generating realistic images. While DCGAN achieves slightly higher IS means (ranging from 2.56 to 3.44, compared to GAGAN's range of 1.96 to 2.06), this could be attributed to a trade-off between image diversity and fidelity. The consistently lower FID scores for GAGAN suggest that its primary focus on producing highly realistic images may occasionally reduce diversity, as reflected in its IS values. Nevertheless, GAGAN's relatively stable IS performance and significantly better FID scores highlight its robustness and overall effectiveness as an advanced generative model for producing high-quality, realistic images. The variances for FID and IS metrics, calculated from the scores presented in Table 5, are summarized in Table 6. These variances provide insight into the variability of the scores across multiple training runs, highlighting the consistency or instability in the models' performance. Table 6 presents IS and FID for both DCGAN and GAGAN across multiple training runs, along with their corresponding variances. The variances provide insight into the model's performance consistency. For DCGAN, the relatively high IS variance (0.1079) and FID variance (29.7179) indicate significant variability in the quality and diversity of generated images across runs. In contrast, GAGAN shows much lower variances for both IS (0.0011) and FID (7.9706), demonstrating greater stability and consistency in performance across training sessions. These results emphasize GAGAN's ability to produce more reliable outputs, while DCGAN's performance shows greater fluctuation. To further illustrate this variability, Figure 9 presents the results with error bars representing the standard error of the mean (SEM) for both IS and FID metrics. These error bars, directly related to the variances in Table 6, provide a clear representation of the uncertainty in the mean metric values. This representation underscores the reliability of the reported improvements, and highlights GAGAN's consistent performance across multiple runs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **FID Variance** & **IS Variance** \\ \hline DCGAN & 29.7179 & 0.1079 \\ GAGAN & 7.9706 & 0.0110 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: FID and IS Variances for DCGAN and GAGAN. Figure 9: Error bars for the standard error of the mean for IS and FID metrics across training runs. In summary, while DCGAN models tend to stabilize around lower generator losses, their higher discriminator losses highlight an imbalance in training both components effectively. GAGAN models, however, demonstrate strong discriminator performance, particularly in GAGAN/6 and GAGAN/3, which achieved the lowest d_loss values. Although GAGAN models display a broader range of g_loss values, reflecting variability in generator performance, they excel in training discriminators that are adept at identifying real versus generated data. This indicates GAGAN's overall superiority in achieving a balanced and effective adversarial training dynamic, leading to improved generative modeling capabilities. Overall, the analysis underscores the effectiveness of the GAGAN framework in enhancing the training dynamics of generative models.