gan-asymptotically-consistent

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

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

GANs are asymptotically consistent estimators of the data distribution due to the universal approximation theorem for neural networks

Dependents

These beliefs depend on this one:

• OUT gan-reliable-for-safety-critical-deployment — GANs can be reliably deployed in safety-critical domains (medical imaging, physics simulation) given their asymptotic consistency guarantees and demonstrated cross-domain applications.
• OUT gan-stable-convergence-guaranteed — GAN training reliably converges to the unique equilibrium where the generator matches the data distribution, given TTUR and asymptotic consistency guarantees.