GAN

A Generative Adversarial Network, or GAN, is like having two AI models compete against each other to get better. One model, called the 'generator,' tries to create new data, say, an image of a cat. The other model, the 'discriminator,' then tries to figure out if that image is real (from a dataset of actual cats) or fake (created by the generator).

This competition makes both models improve. The generator gets better at creating convincing fakes, and the discriminator gets better at spotting them. Eventually, the generator becomes so good that the discriminator can barely tell the difference, resulting in very realistic, newly generated content that wasn't in the original training data. GANs are widely used in creating deepfakes, generating photorealistic images from text, and even enhancing old photos.