Generative Adversarial Networks (GANs) are a groundbreaking technology in the field of artificial intelligence, designed to generate new data that mirrors a given dataset. Generative Adversarial Networks (GANs) comprise two key components: the generator, which creates new data, and the discriminator, which assesses its authenticity. The generator’s goal is to produce data that closely resembles real examples, while the discriminator’s role is to differentiate between genuine and generated data. Through adversarial training, where both networks improve based on each other’s feedback, GANs can produce highly realistic outputs, such as images, videos, or text. This technology has transformative potential across various domains, including art, gaming, and even pharmaceuticals, highlighting its versatility and innovative impact.
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