Generative Adversarial Networks

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Jack Farris 22 Oct 2024

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data samples by pitting two neural networks against each other: a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates its authenticity compared to real data. This adversarial process enables GANs to produce highly realistic images, videos, and audio, making them popular in fields like art, gaming, and simulation. Their ability to learn and adapt has significant implications for creative industries and data augmentation.

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