GAN

Resumen

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.

Grafo de tecnologías

Cómo se conecta GAN con el resto del stack.

Alternativas

• Diffusion technique

  Una alternativa a GAN · source

Comparativas

Comparativas detalladas con las alternativas anteriores.

• GAN vs Diffusion