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Generative Adversarial Networks (GANs)
GANs consist of two neural networks, a generator and a discriminator, locked in a continuous competition. The generator creates synthetic data like images or text, attempting to fool the discriminator into believing it's real, while the discriminator learns to distinguish between real and generated data. This adversarial process drives both networks to improve.
In plain terms
It's like a counterfeit artist (generator) trying to create perfect forgeries, and a detective (discriminator) learning to spot them, both getting better with every attempt.
Why it matters
GANs are pivotal for creating realistic synthetic data, artistic creation, data augmentation, and exploring latent data spaces.
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