## 4 Results and Discussion
### RQ3: What Evaluation Methods Are Used to Assess the Found Architecture?
#### 4.3.3 Supported Generation Type  
NAS-GANs support two primary types of generation tasks: supervised and unsupervised. In unsupervised generation, the GAN creates images without any specific input conditions, using random noise as input to generate diverse images from a general class, such as faces, objects, or scenes. This type is akin to unconditional generation in standard GANs [55]. On the other hand, supervised generation involves guiding the GAN's image creation process with additional input conditions or labels, similar to conditional GANs (eGANs). Here, the generator is conditioned on specific information, such as class labels, images, text, or other modalities, allowing for controlled and targeted image synthesis based on predefined criteria. This structured approach enables the generation of images that adhere to specific requirements, enhancing the utility of GANs in applications requiring precise image outputs [56]. Among the reviewed NAS-GAN methods, the support for conditional (supervised) image generation is explicitly found in AGAN, DEGAS, and NSGA-II with CGP on the STL-10 dataset, MN-NAS and T2 IGAN on CIFAR-10/CIFAR-100, and GAN Compression on image-to-image translation tasks (Horse\(\leftrightarrow\)Zebra, Edges\(\rightarrow\)Shoes, and Cityscapes). AGAN employs a supervised setup to refine GAN architectures, combining the GAN loss with an auxiliary supervised loss to enhance image fidelity. DEGAS integrates domain-specific knowledge into the NAS process, optimizing for specific supervised image generation tasks. NSGA-II with CGP uses multi-objective evolutionary optimization for CNN representations in NAS, improving supervised tasks by balancing complexity and performance. The results of the reviewed literature on STL-10 supervised generation are presented in Table 6. The scores indicate that NSGA-II with CGP performed better than AGAN and DEGAS on the STL-10 dataset. MN-NAS is evaluated on CIFAR-10 and CIFAR-100, achieving an IS of approximately \(9.10\pm 0.11\) and an FID of 13.00 on CIFAR-10, and an IS of about \(8.50\pm 0.10\) with an FID of 16.50 on CIFAR-100. Similarly, T2 IGAN is reported on CIFAR-10 and CIFAR-100, where it achieves an IS of \(9.02\pm 0.08\) and an FID of 10.90 on CIFAR-10, and an IS of \(8.65\pm 0.07\) with an FID of 14.10 on CIFAR-100. GAN Compression, which targets efficient architectures for conditional GANs, is evaluated on several image-to-image translation tasks: for example, it achieves an FID of 14.2 and an IS of 8.95 on the Horse\(\leftrightarrow\)Zebra task, \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **IS \(\uparrow\) * & **FID \(\downarrow\) ** \\ \hline AGAN & 8.82 \(\pm\) 0.09 & 23.8 \\ DEGAS & 8.85 \(\pm\) 0.07 & 9.83 \\ NSGA-II with CGP & 9.22 \(\pm\) 0.05 & 7.24 \(\pm\) 0.08 \\ \hline \hline \end{tabular}  
* The up arrow (\(\uparrow\)) indicates that higher IS values are better. ** The down arrow (\(\downarrow\)) indicates that lower FID values are better.  
\end{table} 
Table 6: Summary of supervised generation results on STL-10 dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Search** & \multicolumn{2}{c}{**CIFAR-10**} & \multicolumn{2}{c}{**STL-10**} \\  & **Strategy** & **IS \(\uparrow\) * & **FID \(\downarrow\) ** & **IS \(\uparrow\) * & **FID \(\downarrow\) ** \\ \hline EAGAN [30] & EA & \(8.81\pm 0.10\) & 9.91 & 10.44 \(\pm\) 0.08 & 22.18 \\ EAS-GAN [29] & EA & \(7.45\pm 0.08\) & 33.2 & - & 38.84 \\
**EWSGAN *[31]** & **EA** & \(8.99\pm 0.11\) & **9.09** & **10.51 \(\pm\) 0.13** & **21.89** \\ NSGA-II with CGP [32] & EA & \(8.89\pm 0.01\) & 16.6 & 10.3 \(\pm\) 0.01 & 26.3 \\ \hline \hline \end{tabular}  
* The up arrow (\(\uparrow\)) indicates that higher IS values are better. *
* The down arrow (\(\downarrow\)) indicates that lower FID values are better.
* Bold values indicate the best performing method across all evaluated approaches.  
\end{table}