## 3 Advancements in Generative Adversarial Network Research
### Unsupervised Generative Adversarial Networks  
Unsupervised GANs, as the name suggests, are trained without any labeled data. They learn to generate samplings from an unknown data distribution by optimizing a generator network and a discriminator network that plays a minimax game [88]. GAN models converge when the latter cannot differentiate whether an image has been artificially generated. One of the main benefits of using GANs is that data generation can be automated. Generating synthetic human faces with a range of features and realism has facilitated the creation of artificial datasets for facial recognition, addressing privacy relations associated with real data [89]. Synthetic data have emerged as a viable alternative for acquiring large datasets, enabling researchers to generate diverse and representative samplings without compromising privacy. The performance of networks trained on synthetic facial recognition data has historically been poor. The domain gap between synthetic and real-world data has frequently been substantial, leading to sub-optimal performance when deploying models in real-world scenarios [68]. Recent advancements in GAN models, particularly StyleGAN2, have showcased significant advancements in generating highly realistic and visually appealing human faces. StyleGAN2 has notably improved the visual quality and pixel resolution and minimized or eliminated artifacts in the generated faces, resulting in a more convincing and authentic appearance [23]. It is very important to mention that the datasets used have real human faces from the FFHQ dataset, which Nvidia proposed as their TensorFlow implementation [9; 39]. The GAN face generation model has achieved the highest synthesis quality in generating realistic human faces. This model can generate faces at a high resolution of \(1024\times 1024\) pixels, resulting in highly detailed and lifelike facial images [9]. Using this model has a big impact because it requires a large amount of computational power. StyleGAN3 improves the weakness of StyleGAN2 by tackling the texture sticking that happened in the morphing transition between faces [9]. Also, positional references are available in the intermediate layers for the network to process feature maps with the next sources: per-pixel noise inputs, positional encodings, image borders, and aliasing (the hardest one to identify and fix) [10; 23]. The main goal was to eliminate all positional references, and this goal was reached by making the network equivariant, where an operation (e.g., ReLU) should not insert any positional references [11]. A substantial training dataset is required to achieve a diverse range of generated data. However, it is crucial for research to investigate how the proportions and quality of this original dataset impact the dispersal of outcome data samplings. The initial models released in StyleGAN utilize the FFHQ and CelebA-HQ datasets, providing models trained at a resolution of \(1024\times 1024\). Aside from these original models, a few unofficial implementations of StyleGAN in other frameworks propose models trained on alternative facial datasets with variable resolutions and quality. Comparing these alternative models with the original StyleGAN is not feasible due to their unofficial nature. This work serves as an initial step and an essential tool in understanding the influence of the original size and quality of the datasets on the quality and dispersal of generated data samplings. A research gap exists concerning the quantity of data and deviation demanded to train StyleGAN adequately and the association between the original training samplings and the generated samplings. This study addresses this gap by examining the relationship between the original training samplings, generated samplings, and various aspects of StyleGAN. The authors trained StyleGAN on diverse face datasets with various resolutions to provide a valuable resource for researchers.