Generative Adversarial Networks

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Generative adversarial networks, or GANs, are deep learning models designed to create new data that looks like it came from a real dataset. They work by pairing two neural networks that learn together through a kind of controlled competition. The generator tries to produce realistic synthetic samples, while the discriminator checks whether each sample is real or fake. As training continues, the generator keeps adjusting to fool the discriminator more effectively, and the discriminator keeps improving at spotting the generator’s mistakes.

This back-and-forth process gradually pushes the generator to create outputs that look increasingly convincing. GANs are used for tasks like generating realistic images, improving image quality, transferring artistic styles, and creating synthetic data for privacy or testing. They remain a core technique in modern generative AI because of their ability to learn complex data patterns and produce high-quality results.

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