GANs Explained: How AI Learns to Create New Data

Generative Adversarial Networks (GANs) are a class of AI models that utilize two competing neural networks to create new data that mimics real data. A Generator network creates synthetic data from random noise, while a Discriminator network attempts to distinguish between real and generated data. This adversarial process iteratively improves both networks, leading to increasingly realistic synthetic data, applicable to tasks like image generation and synthetic data creation.

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