Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning [Machine learning] frameworks and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game [Zero-sum game], where one agent’s gain is another agent’s loss.

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GANs are similar to mimicry in evolutionary biology, with an evolutionary arms race between both networks.

(“Generative Adversarial Network” 2023)

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