Description: Generative Adversarial Networks (GANs) are a class of machine learning frameworks where two neural networks compete against each other to generate new synthetic instances of data that can pass for real data. This approach is based on the interaction of two main components: the generator, which creates fake data, and the discriminator, which evaluates the authenticity of the generated data against real data. Through this competitive process, both networks continuously improve, allowing the generator to produce increasingly realistic data. GANs are particularly relevant in the field of artificial intelligence, as they enable the creation of high-quality images, audio, and text, opening new possibilities in automation and content generation. Their ability to learn complex patterns and generate synthetic data has led to their use in various 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 the creation of language models and multimodal artificial intelligence systems.
History: Generative Adversarial Networks were introduced by Ian Goodfellow and his colleagues in 2014. Since their introduction, GANs have significantly evolved, leading to various variants and improvements that have expanded their applicability across multiple domains. In the following years, numerous papers were published exploring different architectures and techniques to optimize GAN performance, leading to their adoption in both research and industry.
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 the creation of language models and multimodal artificial intelligence systems.
Examples: A notable example of GAN use is the project ‘This Person Does Not Exist’, which generates images of human faces that do not belong to real people. Another example is the use of GANs in the film industry to create realistic visual effects and in fashion to design virtual clothing. Additionally, GANs have been used in music generation and in enhancing the quality of medical images.
