Generator and discriminator trained against each other to synthesise realistic data.
Für diese beiden Technologien sind noch keine vergleichbaren Merkmale erfasst.
Aus den Unterschieden oben durch feste Regeln erzeugt, nicht von einem Modell.
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architecture · Algorithms
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.
technique · Techniques
A diffusion model learns to remove noise step by step, so that sampling starts from pure noise and arrives at a plausible sample. It solves the training instability of adversarial methods by replacing a two-player game with a single denoising objective. It exists because that objective is stable and covers the data distribution well, which is why it became the dominant approach for image generation.
