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A generative architecture that trains two networks against each other — a generator that fabricates data and a discriminator that tries to spot the fakes.
Introduced by Goodfellow et al. (2014), a GAN pits a generator that produces synthetic samples against a discriminator that judges real versus fake. As each improves, the generator learns to produce increasingly realistic output. GANs drove a generation of photorealistic image synthesis.
For image generation they have largely been succeeded by diffusion models, but the adversarial-training idea remains foundational.
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