Generative adversarial network (GAN) is a type of ML algorithm built using a combination of two neural networks (generator and discriminator). One of those networks (G) generates an output (for example, an image), and the second one (D) aims to distinguish “real” outputs from generated ones. This results in a zero-sum game, where one agent's gain is another agent's loss.
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