Generative adversarial networks

Machine learning frameworks that involve two neural networks, the generator and the discriminator, trained simultaneously through adversarial processes to generate realistic data samples. The generator creates data samples, while the discriminator evaluates them, improving the generator's ability to produce increasingly realistic outputs over time. Generative adversarial networks are primarily used by researchers and developers in fields such as image generation, data augmentation, and unsupervised learning.