GAN

Aperçu

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.

Graphe des technologies

Comment GAN se rattache au reste de la pile.

Alternatives

• Diffusion technique

  Une alternative à GAN · source

Comparaisons

Comparaisons détaillées avec les alternatives ci-dessus.

• GAN vs Diffusion