## 4 Results and Discussion
### RQ4: What Are the Gaps in the Research on NAS in GANs? After examining and analyzing the identified NAS-GAN techniques, we have addressed this question with the results discussed in Section 4 and further elaborated on in the study's implications outlined in Section 5. Table 7 provides an overview of the best-performing NAS-GAN methods across key datasets, detailing performance metrics such as IS and FID. This table highlights the diversity in evaluation settings--with methods tested on datasets ranging from MNIST and CelebA to STL-10 and CIFAR-100--and showcases different generation types, including unsupervised, supervised, and conditional approaches. The variation in performance across these datasets suggests that the efficacy of current NAS-GAN approaches is highly sensitive to dataset characteristics and the chosen evaluation metrics. For example, while some methods yield exceptional FID scores on simpler datasets like MNIST or CelebA, their performance does not uniformly translate to more complex datasets. This observation points to a broader gap in the literature: a unified NAS strategy that can reliably generalize across different data distributions and generation scenarios remains elusive. The key gaps and areas for future research include:  
* Current NAS for GANs systems primarily focus on automated architecture generation for both Generator and Discriminator networks or exclusively for Generator networks. However, an approach that concentrates on searching for superior Discriminator networks has yet to be introduced. This represents a significant opportunity for future research to explore dedicated Discriminator architecture search methods, which could lead to enhanced overall GAN performance. * Another significant gap is the reliance on CIFAR-10 and STL-10 datasets for evaluating GAN architecture performance. While these datasets allow for direct comparisons, they limit the generalizability of the findings. Very few systems have used datasets like CelebA and LSUN for validation. Therefore, future research should aim to conduct performance evaluations on a larger variety of image datasets, including CelebA, LSUN, and COCO, to provide a more comprehensive assessment of GAN performance. * Most existing NAS-GANs systems have been developed for unconditional image generation tasks. To broaden the scope and impact of NAS for GANs, future work should also focus on other types of image generation, such as conditional image generation and image-to-image translation tasks. This expansion would not only enhance the versatility of GANs but also potentially uncover new applications and benefits of NAS in different image generation contexts. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Dataset** & **Generation Type** & **IS \(\uparrow\)*** & **FID \(\downarrow\)** \\ \hline Mustangs & MNIST & Unsupervised & - & 42.24 \\ Mustangs & CelebA & Unsupervised & - & 36.15 \\ EWSGAN & CIFAR-10 & Unsupervised & 8.99 & 9.09 \\ EWSGAN & STL-10 & Unsupervised & 10.51 & 21.89 \\ NSGA-II with CGP & STL-10 & Supervised & 9.22 & 7.24 \\ T2 IGAN & CIFAR-100 & Conditional & 8.65 & 14.10 \\ GAN Compression & Horse\(\rightarrow\)Zebra & Conditional & 8.95 & 14.20 \\ \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 7: Best-performing NAS-GAN Methods on Key Datasets. * There is also a need for more robust evaluation metrics beyond just the Inception Score and FID. While these metrics are commonly used, they may not capture all aspects of GAN performance. Future research should develop and incorporate new metrics that can provide a more holistic evaluation of GAN quality, including aspects like diversity, fidelity, and realism. * Another area that requires more exploration is the interpretability and explainability of the generated GAN architectures.