gan-two-network-minimax-game

IN premise — entries/2026/06/21/wiki-Generative_adversarial_network-chunk-1.md

Created 2026-06-21T09:55:49+00:00

A GAN consists of exactly two neural networks (generator and discriminator) trained as a minimax zero-sum game where the generator minimizes and the discriminator maximizes the same objective function

Dependents

These beliefs depend on this one:

• IN gan-architectural-asymmetry-mirrors-game-theory — GAN architectural asymmetry (deconvolutional generator vs convolutional discriminator in image tasks) parallels its game-theoretic asymmetry — the discriminator has a closed-form optimal solution while the generator does not, and these two asymmetries coexist in the minimax game structure, though the antecedents do not establish that the architectural choices were designed to instantiate the mathematical asymmetry.