generative adversarial networks (gans)
Generative Adversarial Networks (GANs) are a framework consisting of two opposing neural networks, a generator and a discriminator, that work together to generate data. The generator network creates synthetic data samples from random noise, while the discriminator network compares these generated samples with real data to classify them as real or fake. The networks learn from each other by continuously competing against one another, resulting in the generator gradually improving its ability to produce realistic data, while the discriminator becomes better at distinguishing between real and generated data.
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