Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data instances that resemble a given dataset. They consist of two neural networks, the generator and the discriminator, that work against each other in a game-like scenario, where the generator creates data and the discriminator evaluates it for authenticity. This innovative structure has revolutionized the fields of digital media art, enabling artists and creators to explore new possibilities in image generation, style transfer, and even video synthesis.
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