## 4 Results and Discussion
### RQ3: What Evaluation Methods Are Used to Assess the Found Architecture?  
Architecture Evaluation in NAS-GAN methods involves various datasets and metrics. Here we discuss evaluation metrics and present results from various NAS-GAN  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Search** & **Searched** & **Architecture Modification** & **Optimization** & **GPU** & **GPU** & **Space** & **Space** \\  & **Strategy** & **Network** & **Technique** & **Objective** & **Type** & **Days** & **Type** & **Size** \\ \hline AutoGAN & RL & G Only & \begin{tabular}{c} RNN \\ Controller \\ \end{tabular} & IS & 2080 Ti & 2 & Cell & \(10^{5}\) \\ AGAN & RL & G and D & \begin{tabular}{c} RNN \\ Controller \\ \end{tabular} & IS & Titan-X & 1200 & Cell & \(10^{5}\) \\ MN-NAS & RL & G Only & MDP & IS & 1080 Ti & - & Cell & \(10^{27}\) \\ E\({}^{2}\)GAN & RL & G Only & MDP & IS \& FID & 2080 Ti & 0.3 & Cell & - \\ T2 IGAN & RL & G Only & MDP & IS \& FID & V100 & 0.42 & Cell & \(10^{25}\) \\ \hline AdversarialNAS & Gradient & G and D & - & GAN objective & \begin{tabular}{c} 2 X \\ 2080 Ti \\ \end{tabular} & 1 & Cell & \(10^{38}\) \\ DEGAS & Gradient & G Only & - & Reconstruction loss & Titan-X & 4 & Chain & \(10^{8}\) \\ GAN Compression & Gradient & G Only & - & \begin{tabular}{c} GAN Objective + \\ reconstruction loss \\ \end{tabular} & 2080 Ti & - & Cell & \(10^{9}\) \\ alphaGAN & Gradient & G Only & - & \begin{tabular}{c} Duality Gap Loss \\ \end{tabular} & Tesla & 0.15 & Cell & \(10^{11}\) \\ DAMGAN & Gradient & G Only & \
\begin{tabular}{c} Dual-Attention \\ Mechanisms \\ \end{tabular} & GAN objective & 3090 & 0.09 & Cell & - \\ \hline EvoGAN & EA & G and D & Mutation & Custom & NA & - & Hybrid & - \\  & & & Mutation \& & & & & \\ Bi-GAN & EA & G Only & Continuous & Accuracy & NA & - & Hybrid & - \\  & & Refinement & & & & & \\ NSGA-II DCGAN & EA & G Only & Crossover \& & Custom & - & - & Chain & - \\ E-GAN & EA & G and D & Mutation & Custom & 1080 TI & 1.25 & - & - \\ Lipizzaner & EA & G and D & Mutation & GAN objective & - & - & Cell & - \\ Mustangs & EA & G and D & Mutation & GAN objective & - & - & Cell & - \\ COEGAN & EA & G and D & Mutation & FID \& & - & - & Chain & - \\  & & & Discriminator loss & & & & & \\ EAGAN & EA & G and D &
\begin{tabular}{c} Crossover \& \\ Mutation \\ \end{tabular} & IS - FID & NA & 1.2 & Cell & \(10^{38}\) \\ EAS-GAN & EA & G Only & Mutation & Custom & 3090 & 1 & Cell & - \\ EWSGAN & EA & G Only & Crossover \& & IS - FID & 2080 Ti & 1 & Cell & \(10^{15}\) \\  & & Mutation & & & & & \\ NSGA-II with CGP & EA & G and D & Crossover \& & & & & & \\ \hline \hline \end{tabular}  
* The optimization objective (IS&FID) combines both IS and FID into a composite objective, rather than using them independently (IS-FID). The search space size for T2 IGAN is estimated based on a per-cell space of \(5^{6}\approx 1.6\times 10^{4}\) configurations, stacked over 6 cells.  
\end{table}
Table 3: Comparison of different GAN architecture search methods.  
works as reported in literature. Then, we examine the datasets used for search, training, and testing, including those that evaluate transferability, highlighting the current benchmark for comparing NAS-GAN methods. Finally, we outline the generation tasks trained on these datasets.