generative adversarial networks (gan)
Generative adversarial networks (GANs) are machine learning models comprised of two neural networks: a generator and a discriminator. The generator tries to create synthetic data (such as images) that resemble the original, while the discriminator aims to distinguish between the real and synthetic data. Both networks learn and improve over time through competition, with the generator continually trying to produce more realistic data and the discriminator becoming better at distinguishing between real and fake. GANs are commonly used for tasks like data generation, image synthesis, and style transfer.
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