Description: An Adversarial Network is a type of neural network trained to generate adversarial examples, that is, data that has been modified in such a way that it deceives other machine learning models. These networks consist of two main parts: a generator and a discriminator. The generator creates fake examples from random noise, while the discriminator evaluates whether the examples are real or generated. This competitive process between both models is what gives rise to the term ‘adversarial’. Generative Adversarial Networks (GANs) are an innovative approach in the field of generative models, allowing the creation of synthetic data that can be indistinguishable from real data. The ability of these networks to learn complex patterns and generate new content has revolutionized various areas, from image generation to voice synthesis, opening a wide range of possibilities in artificial intelligence and deep learning.
History: Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow and his colleagues in 2014. This innovative approach emerged as a solution to data generation problems in deep learning. Since their introduction, GANs have evolved and diversified into multiple variants, each improving specific aspects such as training stability and the quality of generated data.
Uses: Adversarial Networks are used in various applications, including image generation, image quality enhancement, digital art creation, voice synthesis, and text generation. They are also employed in fraud detection and in improving machine learning models by generating synthetic data for training.
Examples: A notable example of the use of Adversarial Networks is the project ‘This Person Does Not Exist’, which generates images of human faces that do not exist in reality. Another case is the use of GANs in creating original artworks, where models can learn the styles of famous artists and generate new pieces that mimic those styles.
