Generative Adversarial Networks

Generative adversarial networks (GANs) are a class of machine learning models used to generate realistic synthetic data such as images, audio or video. A GAN consists of two neural networks – the generator and the discriminator – that are trained simultaneously in a competitive setup. The generator creates synthetic outputs, while the discriminator evaluates whether the outputs are real or fake. Through this adversarial process, the generator learns to produce increasingly realistic data. GANs are widely used in image synthesis, deepfake creation, data augmentation and creative design applications, and they play a foundational role in advancing generative artificial intelligence capabilities.