Generative Adversarial Networks (GANs) are a class of machine learning frameworks introduced by Ian Goodfellow and his colleagues in 2014. They consist of two neural networks—the generator and the discriminator—that are trained simultaneously through adversarial processes. The generator creates synthetic data resembling real data, while the discriminator evaluates whether the data is real or generated. This adversarial training enables GANs to produce highly realistic data, making them valuable in applications such as image generation, data augmentation, and more.
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