## 2 What Is a Generative Adversarial Network?
### Main Data Repositories Utilized
The dataset is designed to exhibit diversity in various aspects, including facial pose, occlusion, ethnicity, age, background, and scene type. The scenes captured in the dataset encompass a range of contexts, such as traffic, sports events, and outdoor activities. Regarding facial poses, the FDF dataset offers more diversity than the FFHQ and Celeb-A datasets, although its resolution is lower than that of the FFHQ dataset. The primary objective of the FDF dataset is to facilitate the advancement of face recognition algorithms by providing a publicly available resource that includes source code and pre-trained networks. The face images in the FDF dataset were extracted from a pool of 1.08 million images in the YFCC100-M dataset, and the annotations were generated using state-of-the-art models for accurate and reliable results [45]. The FDF dataset, with its high-quality annotations and diverse range of faces, provides a valuable resource for advancing face recognition algorithms. These studies indicate a need for a deeper understanding of the current state of datasets used in GANs. The findings presented in [46] suggest that while most GAN models can achieve similar performance scores with good hyperparameter optimization and random restarts, further improvements can be obtained by allocating a larger computational budget and conducting extensive tuning rather than relying solely on fundamental algorithmic changes. Study [47] found that the existing measures for assessing GAN performance must be adequate for the task at hand. Ref. [48] found that there needs to be a measure to quantify the failure modes of GANs. Together, these papers suggest that the current state of datasets for GANs needs to be better understood. Further research is needed to develop better methods for assessing GANs' performance. The next papers suggest that there are challenges in creating GAN datasets. The authors in [46] concluded that determining the superiority of one GAN algorithm over another is challenging. Their findings indicated that most GAN models could acquire similar performance scores given sufficient hyperparameter optimization and random restarts. This suggests that the comparative evaluation of GAN algorithms requires careful consideration and thorough experimentation to discern meaningful differences in their performance. It suggests that there needs to be a clear consensus on which GAN algorithm is best and that improvements may come from more computational resources rather than fundamental algorithmic changes. Ref. [49] found that data generated by a GAN cannot statistically be better than the data they were trained on. This suggests that there are limitations to what GANs can achieve. Also, the research of [4] found that generating images of an unprecedented quality is possible using a progressive, growing method for training GANs. Regarding the datasets used in face verification research provides insights into various options [50]. For instance, Labeled Faces in the Wild (LFW) ([[https://github.com/securifai/masked_faces](https://github.com/securifai/masked_faces)]([https://github.com/securifai/masked_faces](https://github.com/securifai/masked_faces)), accessed: 25 June 2023) is a commonly used dataset consisting of 13,233 face images from 5749 individuals, serving as a standard benchmark for evaluating face verification algorithms under unconstrained conditions. Another widely used datasetis CASIA-WebFace ([[https://github.com/securifai/masked_faces](https://github.com/securifai/masked_faces)]([https://github.com/securifai/masked_faces](https://github.com/securifai/masked_faces)), 26 June 2023), which contains many identities (10,575) and facial data samplings (494,414), making it a popular choice as a baseline dataset for face verification tasks. CelebFaces+ has 202,599 images of 10,177 identities, and its annotated version, CelebA, has five landmark locations and 40 binary attributes annotated. MegaFace has 4.7 million samplings from 672,057 identities but has limited variations per identity.