Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a type of deep learning model that consists of two neural networks: a generator and a discriminator. The generator network generates new data instances, while the discriminator network evaluates the authenticity of the generated data. The two networks are trained together in a zero-sum game, where the generator tries to produce data that can fool the discriminator, and the discriminator tries to correctly identify whether the data is real or fake. As the training progresses, the generator learns to produce more realistic data, while the discriminator becomes better at identifying fake data. GANs have been used for a variety of applications, including image and video generation, text-to-image synthesis, and data augmentation.
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