**Description:** Generative Adversarial Networks (GANs) are a type of machine learning model that relies on the interaction between two neural networks: a generator and a discriminator. The generator’s task is to create fake data that mimics a real dataset, while the discriminator evaluates this data, determining whether it is real or generated. This competitive process between both networks allows the generator to continuously improve its ability to create data that is indistinguishable from real data. GANs are particularly relevant in the field of artificial intelligence, as they have proven effective in generating images, audio, and text, among others. Their architecture allows for unsupervised learning, meaning they can learn complex patterns without the need for explicit labels. This feature makes them a powerful tool for content creation and data simulation, opening up new possibilities in various technological and creative applications.
**History:** Generative Adversarial Networks were introduced by Ian Goodfellow and his colleagues in 2014. Since their publication, they have rapidly evolved, leading to various variants and improvements in their architecture. The original idea was inspired by game theory, where the generator and discriminator compete against each other, leading to an equilibrium where the generator produces high-quality data. Over the years, GANs have been the subject of numerous studies and applications, becoming an active area of research in the field of deep learning.
**Uses:** GANs are used in a variety of applications, including image and video generation, image resolution enhancement, digital art creation, voice synthesis, and text generation. They are also applied in data simulation to train other machine learning models, as well as in 3D model creation and medical image quality enhancement.
**Examples:** A notable example of GAN is the project ‘This Person Does Not Exist’, which uses a GAN to generate images of human faces that do not belong to real people. Another example is the use of GANs in medical image enhancement, where high-resolution images are generated from lower-quality images to assist in medical diagnostics.
