## 3 Advancements in Generative Adversarial Network Research
### Supervised Generative Adversarial Networks
\begin{table}
\begin{tabular}{c|l l l} \hline
**Ref.** & **Achitecture** & **Datasets** & **Metrics** \\ \hline
[73] & GAN & ImageNet, CIFAR 10, STL 10 & FID, BS, MA, RA \\ \hline
[47] & WGAN-GP & CIFAR 10, CIFAR 10, ImageNet, MNIST & FID, BS, GAN-Train, GAN-Text \\ \hline
[48] & DGGAN & CIFAR 10, CelebA-HQ, LSUN bedroom & FID, BS, KD, MS-SSM \\ \hline
[93] & StoryGAN & CIFAR 10, CelebA-HQ, LSUN bedroom & MS-SSM \\ \hline
[17] & WGAN, WPGAN, MSGAN & BaTS & SSIM, PSNR \\ \hline
[94] & GAN & MNIST, HAR, LST & FID, BS \\ \hline
[95] & WGAN,GAN & AlexNet, RAF-D8 & W-DGSTANCE \\ \hline
[69] & WGAN-GE, BigGAN & CIFAR 10 & FID, BS, W-DGSTANCE \\ \hline
[68] & DGGAN, WGAN, PrGAN, StyleGAN, BigGAN, CoGeGAN & FPRR, CelebA, LSUN & RGB, HSV, YGYC \\ \hline
[50] & StyleGAN & CelebA, CASIA WebFace & FID, BS \\ \hline
[71] & StyleGAN & CIFAR 10, STL 10, ImageNet, CASIA IWDR10 & FID, BS \\ \hline
[72] & StyleGAN & CIFAR 10 & FID \\ \hline
[73] & FullGAN, SNDCGAN, BigGAN & CIFAR 10 & FID \\ \hline
[74] & Enlighten-GAN & Sentinel-2 & CSM, PL PSNR, LPIPS \\ \hline
[75] & SPGAN & VOCFace2, CelebA, Helen, LWW, CP-FP, AgeDB-30 & FID, PSNR, SSIM \\ \hline
[76] & PCGAN, StyleGAN, CoGeGAN & Inria Aerial & FID, XID \\ \hline
[77] & PF-GAN & Celeb-HD & Similarity Score \\ \hline
[78] & GAN & VoxCelebA & PSNR, SSIM \\ \hline
[79] & StyleGAN & SHHQ & FID \\ \hline
[77] & FCG-GAN & CelebA & PSNR, SSIM \\ \hline
[78] & StyleGAN, L & FFHQ, Pedemon & FID, BS \\ \hline
[79] & DDMGAN & MNIST & FID, KL-divergence \\ \hline
[78] & StyleGAN & CelebA-HQ & FID, LPIPS, SSIM, PSNR, MSE \\ \hline
[78] & StyleGAN2 & CelebA, LPW, FFHQ, Caltech & LPIPS, MSE \\ \hline
[79] & StyleGAN & FFHQ & FID, BS, LPIPS, MS-SSIM \\ \hline
[79] & InferFaceGAN & CelebA & KL-divergence \\ \hline
[78] & PCGAN, InferFaceGAN & CelebA-HQ & ACRD, ASCD, SRC \\ \hline
[79] & AgeTransGAN & FFHQ-Aging & FID \\ \hline \end{tabular}
\end{table} 
Table 3: Succinct outline of the metrics, datasets, and architectures employed. One of the factors contributing to this limitation is the intrinsic complexity of facial features and their variability in the human population. Human faces possess a wide range of unique features and subtleties, such as the shape of the eyes, the contour of the nose, and the structure of the lips, among other details. The accurate generation of these features and their appropriate blending requires an advanced understanding and modeling of facial variability and structure. In addition, the representation of skin texture and other microscopic details also presents significant challenges. The texture of human skin is highly complex and varies across different regions of the face, with features such as pores, wrinkles, and subtle patterns. Realistic imaging must capture these details in their proper context and achieve a natural and believable appearance. Current methods in GAN have addressed these limitations by using latent coding models and incorporating style and structure techniques in image generation. These techniques allow for greater control and manipulation of facial features, improving the quality and realism of the generated images. However, there is still room for improvement in consistency and fidelity in the representation of complex facial features and their seamless blending. Analysis of the existing literature provides a clear insight into using image datasets in GANs. Many researchers need to filter adequately and segment datasets when using GANs. This is due to excessive confidence in the capacity of these artificial intelligence models, which, although in some cases produce outstanding results, in others, still need to meet expectations. A prominent example of this phenomenon is the StyleGAN architecture, which has been shown to achieve impressive results in generating synthetic images. However, it is also notable that this architecture can generate images with significant defects. This suggests that the imaging process is largely left to chance without paying enough attention to the quality of the data provided to the GAN.