Generative Adversarial Networks (GANs) are a class of AI models that consist of two neural networks a generator and a discriminator working in tandem to produce realistic, high-quality data. GANs are widely used for generating images, videos, and even synthetic data. They’re instrumental in fields such as entertainment, art, and healthcare, as well as applications like image super-resolution, facial recognition, and data augmentation. Generative adversarial networks continue to shape advancements in machine learning by driving realistic data generation and pushing creative boundaries in AI.