Generative Adversarial Networks (GANs) are a powerful technique for semi-supervised and unsupervised learning, enabling the implicit modeling of high-dimensional data distributions. GANs consist of two competing networks: a generator and a discriminator. The generator creates synthetic samples, while the discriminator distinguishes between real and fake samples. Both networks are trained simultaneously, with the generator aiming to produce samples that are indistinguishable from real data, and the discriminator aiming to improve its accuracy in distinguishing real from fake samples. This review provides an overview of GANs, including their architecture, training methods, and applications. It highlights the challenges in GAN training, such as mode collapse and instability, and discusses techniques to address these issues. GANs have been applied to various tasks, including image synthesis, semantic image editing, style transfer, super-resolution, and classification. The review also explores the structure of the latent space and the potential of GANs in computer vision applications, such as image classification, image-to-image translation, and super-resolution. Despite the progress, several open questions remain, particularly regarding the stability and evaluation of GANs.Generative Adversarial Networks (GANs) are a powerful technique for semi-supervised and unsupervised learning, enabling the implicit modeling of high-dimensional data distributions. GANs consist of two competing networks: a generator and a discriminator. The generator creates synthetic samples, while the discriminator distinguishes between real and fake samples. Both networks are trained simultaneously, with the generator aiming to produce samples that are indistinguishable from real data, and the discriminator aiming to improve its accuracy in distinguishing real from fake samples. This review provides an overview of GANs, including their architecture, training methods, and applications. It highlights the challenges in GAN training, such as mode collapse and instability, and discusses techniques to address these issues. GANs have been applied to various tasks, including image synthesis, semantic image editing, style transfer, super-resolution, and classification. The review also explores the structure of the latent space and the potential of GANs in computer vision applications, such as image classification, image-to-image translation, and super-resolution. Despite the progress, several open questions remain, particularly regarding the stability and evaluation of GANs.