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A Generative Adversarial Network (GAN) is a machine learning model that uses two neural networks, a generator and a discriminator, to create new data instances that resemble the training data. The generator attempts to create data that looks real, while the discriminator tries to distinguish between real and generated data, leading to a competitive training process.