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Generative Adversarial Networks Advanced

Two networks in a contest: a generator invents samples, a discriminator judges them. Visualise both and the realness score.

3 lessons 9 quiz questions ⚡ Live compiler

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1 A forger versus a detective

A Generative Adversarial Network trains two networks against each other: the generator turns random noise into samples, and the discriminator tries to tell real from fake. Change the noise below and watch a sample get generated and scored.

2 The adversarial game

The two play a minimax game: the discriminator maximises its accuracy while the generator minimises it by producing ever more convincing fakes. At the ideal equilibrium the generator’s samples are indistinguishable from real data and the discriminator outputs ≈0.5.

const sigmoid = x => 1 / (1 + Math.exp(-x));
// discriminator score for a sample value
function discriminate(x, w, b) { return sigmoid(w * x + b); }
console.log(discriminate(0.3, 2.0, -0.5)); // probability the sample is 'real'

3 Power, pitfalls and uses

GANs produce strikingly realistic images, art, super-resolution and data augmentation. They can be tricky to train: mode collapse (the generator makes only a few outputs) and unstable dynamics are common challenges addressed by variants like DCGAN, WGAN and StyleGAN.

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