## 2 What Is a Generative Adversarial Network?
### Main Data Repositories Utilized  
The datasets commonly used with GANs include:  
* _Flickr Faces HQ (FFHQ)_: This collection of high-quality images featuring human faces is sourced from the photo-sharing platform Flickr. This dataset encompasses a substantial compilation of 70,000 human face images, each with a resolution of \(1024\times 1024\) pixels. FFHQ exhibits significant diversity in age, ethnicity, and image backgrounds, providing a rich and varied set of training data for GANs [39]. Researchers and practitioners often leverage the FFHQ dataset to train GAN models in face image generation and manipulation. Its extensive facial characteristics and high-resolution nature make it an ideal resource for training models that aim to generate realistic and diverse human face images. By utilizing datasets like FFHQ, GAN models can learn from a vast collection of human face images, capturing the nuances and complexities inherent in facial appearances. These datasets play a crucial role in advancing the capabilities of GANs and enabling their applications in various domains, including computer vision, graphics, and facial recognition systems [11]. In Ref. [40], the authors present a novel framework named AgeTransGAN for addressing facial age transformation across significant age differences, including progression and regression. The AgeTransGAN framework comprises two primary components: a generator and a conditional multitask discriminator with an embedded age classifier. The generator utilizes an encoder-decoder architecture to synthesize images, while the conditional multitask discriminator assesses the authenticity and age information of the generated images. The primary goal of AgeTransGAN is to disentangle the identity and age attributes during the training process. This disentanglement is achieved by incorporating various techniques, including cycle-generation consistency, age classification, and cross-age identity consistency. By effectively separating the identity and age characteristics, AgeTransGAN aims to enable more accurate and controllable age transformation across significant age gaps. * _ImageNet_: An extensively annotated, large-scale dataset utilized for visual object recognition research. It comprises an impressive collection of over 14 million images, encompassing a wide range of more than 20,000 distinct object categories [41; 42]. * _CelebA_: A unique dataset in face attribute analysis that encompasses a substantial collection of 200,000 celebrity images. Each image in the dataset is meticulously annotated with 40 attribute labels, making it a valuable resource for tasks related to face  
Figure 3: A general overview of application domains of GANs. image generation and manipulation [43]. CelebA-HQ and CelebAMask-HQ developed as extensions of CelebA, are also extensively employed in similar applications. * _LSUN_: An acronym for large-scale scene understanding, LSUN consists of an impressive assortment of approximately one million labeled images. This dataset is organized into ten distinct scene categories, including memorable scenes like bedrooms, churches, and towers. Moreover, LSUN encompasses twenty object classes, such as birds, cats, and buses. The images explicitly belonging to the church and bedroom scenes, as well as those representing cars and birds, are frequently employed in GAN inversion methods. In addition to the datasets mentioned above, several other resources are employed in GAN inversion studies. These include DeepFashion, Anime Faces, and StreetScapes. These datasets play a pivotal role in conducting comprehensive experiments to assess the efficacy of GAN inversion techniques [44]. * _Flick Diverse Faces (FDF)_: This comprises a vast collection of 1.47 million human faces with a resolution of 128 \(\times\) 128 or higher. Each face in the dataset is accompanied by a bounding box and keypoint annotations, offering precise localization information.