Generative✓ Mathematical
◆ The PatternTwo networks competing: generator vs discriminator
GANs pit a Generator G against a Discriminator D in a minimax game.
min_G max_D V(D,G) = E[log D(x)] + E[log(1−D(G(z)))]
Minimax objective: at equilibrium D(x) = 0.5 everywhere.
// GAN training dynamics
Mode collapse: The biggest GAN failure — G learns only a few convincing samples. WGAN and gradient penalty (WGAN-GP) are the standard fixes.
Pattern bridge: The generator-discriminator game is two opposing forces that produce something neither could alone. In markets, contrarian vs. herd is the same adversarial dynamic.