Generative Adversarial Networks (GANs)

So wird trainiert

A minimax adversarial objective couples two networks. Practical variants change losses, regularization, normalization, and conditioning to stabilize training and control outputs.

So läuft die Inferenz

Sample a latent and run the generator once, making generation fast. Inversion or editing requires additional machinery because there is no built-in iterative reverse process.

Stärken

• •Fast one-pass sampling
• •Sharp outputs in well-scoped visual domains
• •Useful conditional and image-to-image variants

Zielkonflikte

• •Training instability and mode collapse
• •Coverage and likelihood are hard to assess
• •Large open-domain text-conditioned generation has shifted toward other families

Geeignet, wenn

• Low-latency sampling matters in a constrained domain
• A proven GAN pipeline already fits the data and controls
• Diversity and coverage are explicitly evaluated

Vermeiden oder hinterfragen, wenn

• Stable training and broad mode coverage are primary requirements
• The team lacks domain-specific evaluation
• A newer diffusion or autoregressive baseline is not being compared