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Generative AI
GANs
Two networks compete: one generates, one judges, until the fakes are convincing.
A generator invents samples and a discriminator tries to tell them from real data. Each improves against the other, and at equilibrium the generator produces samples the discriminator cannot separate.
They produce very sharp images but are unstable to train, with mode collapse a constant risk. Diffusion models have largely replaced them for image generation, though the adversarial idea recurs widely.
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