GAN

Überblick

A generative adversarial network trains two models against each other: a generator that produces samples and a discriminator that tries to tell them from real ones. It solves the problem of generating sharp, realistic data without an explicit likelihood. It exists because a learned critic gives a much more useful training signal for images than a per-pixel loss, which tends to produce blur.

Technologiegraph

Wie GAN mit dem übrigen Stack zusammenhängt.

Alternativen

• Diffusion technique

  Eine Alternative zu GAN · source

Vergleiche

Detaillierte Gegenüberstellungen mit den Alternativen oben.

• GAN vs Diffusion