gan-theory-practice-alignment

OUT derived (depth 2)

Created 2026-06-21T10:06:02+00:00

GAN theory and practice are aligned — the complete analytical characterization (optimal discriminator, JSD minimization, unique equilibrium) accurately predicts GAN behavior in real-world deployment across physics simulation, medical imaging, and creative applications.

Justifications

SL — Theory-practice alignment holds only when training instabilities (mode collapse, vanishing gradient) are absent

Antecedents (all must be IN):

IN gan-complete-theoretical-characterization — GAN theory provides a complete analytical characterization of optimal behavior: the optimal discriminator has a closed-form solution (Radon-Nikodym derivative), the objective implicitly minimizes Jensen-Shannon divergence, and at the unique equilibrium the generator exactly recovers the data distribution.
IN gan-cross-domain-practical-impact — GANs have demonstrated practical impact across radically different domains — accelerating particle physics simulations at CERN, generating privacy-preserving synthetic medical images, and producing auction-worthy art ($432,500 for Edmond de Belamy) — making them among the most broadly applied generative models.

Unless (any of these IN defeats this justification):

IN gan-mode-collapse-vs-vanishing-gradient — Mode collapse (generator produces limited modes) and vanishing gradient (generator cannot learn) are opposite GAN failure modes caused by the discriminator being too weak vs. too strong, respectively