GANs (Generative Adversarial Networks)

GANs work like a counterfeit money operation where one person creates fake bills while another spots them. As they compete, the counterfeiter gets better at making convincing fakes and the detective improves, until the fakes become nearly indistinguishable from real currency.

In technical terms, a GAN consists of two networks—a Generator that transforms random noise into samples (like images) and a Discriminator that determines whether samples are real or generated. This adversarial process pushes both networks to improve.

For beginners: Imagine copying famous artworks to improve your painting. A strict teacher critiques your work until your copies resemble the originals. In GANs, the Generator and Discriminator push each other to improve until generated samples closely resemble true data.

Key applications: Photo-realistic face generation, synthetic medical images, image super-resolution, sketch-to-photo translation, and artistic style creation.