GAN
A generative architecture where a generator and discriminator network compete against each other to produce highly realistic synthetic data samples for tasks.
Think of It Like This
Like an art forger constantly trying to paint better fake masterpieces while an art detective continuously gets better at spotting the microscopic flaws.
Generative Adversarial Networks (GANs) utilize a minimax game theoretical framework. The generator attempts to fool the discriminator, while the discriminator learns to distinguish between real and synthetic data. Although GANs can produce incredibly sharp images, they are notoriously difficult to train and frequently suffer from issues like mode collapse.