GAN

Обзор

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.

Граф технологий

Как GAN связана с остальным стеком.

Альтернативы

• Diffusion technique

  Альтернатива для GAN · source

Сравнения

Подробные сопоставления с альтернативами выше.

• GAN vs Diffusion