“Their secret lies in the way two neural networks work together — or rather, against each other. You start by feeding both neural networks a whole lot of training data and give each one a separate task. The first network, known as the generator, must produce artificial outputs, like handwriting, videos, or voices, by looking at the training examples and trying to mimic them. The second, known as the discriminator, then determines whether the outputs are real by comparing each one with the same training examples.

Each time the discriminator successfully rejects the generator’s output, the generator goes back to try again. To borrow a metaphor from my colleague Martin Giles, the process “mimics the back-and-forth between a picture forger and an art detective who repeatedly try to outwit one another.” Eventually, the discriminator can’t tell the difference between the output and training examples. In other words, the mimicry is indistinguishable from reality.”

quoted credit Medium