## 3 Advancements in Generative Adversarial Network Research
### Unsupervised Generative Adversarial Networks
This research enables the exploration of various aspects of StyleGAN and the impact of the length and quality of the original dataset on the generated data samplings. Furthermore, it serves as a crucial step toward constructing small and scalable datasets of synthetic facial data. The study investigates the practical training requirements of StyleGAN, the distinction between the authentic and synthetic data samplings, and the relationship between the generated samplings [50]. It is important to note that GANs can be adjusted to improve synthetic facial data generation rules. The association between the training dataset and the generated data is crucial, as a more extensive training dataset leads to more significant variations in the output. StyleGAN, a widely employed deep learning model for generating high-quality images, has been trained on diverse subjects such as anime, cars, cats, bedrooms, and more. The original StyleGAN models were trained on the FFHQ and CelebA-HQ datasets, which had relatively modest sizes of 70,000 and 30,000 samplings, respectively, and produced outputs at a resolution of 1024 \(\times\) 1024. While there are alternative implementations of StyleGAN using different frameworks like PyTorch, they are unofficial and should be compared to the original StyleGAN [23]. StyleGAN has demonstrated linear and disentangled arrangement of its latent space, allowing for traversal directions that selectively modify specific image properties without affecting others. However, these editing capabilities are limited to the latent space of the StyleGAN model and apply only to images generated by the model itself. Recent research efforts have aimed to bridge the gap between the training and target domains by fine-tuning the StyleGAN generator and modifying its weights. This enables the utilization of the disentanglement properties of StyleGAN for assignments such as unsupervised segmentation mask generation. Other investigations have also explored the extraction of segmentation maps using the structure of StyleGAN. By training a StyleGAN model on a specific disentangled axis, researchers have shown that it is possible to overcome domain-related limitations and adapt the generator to suit specific requirements [72]. In [90], a unique application of StyleGAN is presented, diverging from the conventional focus found in other studies. The authors introduce a modified variant called StyleGAN-XL, which exhibits the ability to invert and manipulate images beyond the limited scope of portraits or specific object classes. This extended model demonstrates smooth interpolations between samplings belonging to different semantic categories. The primary objective of the study is to train a StyleGAN3 generator on the large-scale ImageNet dataset successfully. Success in this context is defined by the quality of generated samplings, primarily evaluated using the IS and the diversity measured by the FID. The contributions made by the authors enable the training of a significantly larger model compared to previous attempts while simultaneously reducing the computational requirements. Specifically, the proposed model exhibits three times greater depth and parameter count than a standard StyleGAN3 architecture. This hybrid model effectively combines the distinct semantic properties from both input domains, thus allowing for style mixing. While style mixing is commonly employed within a single domain, such as combining two human portraits, the authors expand its application to encompass cross-domain image manipulation and inversion. The authors in [70] present a new method for detecting GAN-generated images based on analyzing a picture from an inversion process of the GAN. The inversion procedure projects the image under research into the GAN's latent space and back into the image space, producing a resulting image that is compared with the authentic using similitude metrics.