Generative Adversarial Networks
Generative adversarial networks (GANs) are a class of machine learning frameworks designed for generating new data samples that resemble a given training dataset. Comprising two neural networks—generator and discriminator—GANs operate in opposition: the generator creates new data while the discriminator evaluates its authenticity. This process fosters the generation of realistic images, audio, and other data types. GANs have applications across various fields, including art, video game design, and synthetic data creation, demonstrating their potential to revolutionize content generation.
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