Generative Adversarial Networks (GAN)

Editorial record

Plain-language summary

Goodfellow and colleagues set up a game between two networks: a generator that turns noise into samples and a discriminator that tries to tell real data from generated data. Training the two in competition drives the generator to match the real data distribution without needing an explicit likelihood. This adversarial framework produced sharp, realistic image generation and spawned a large family of follow-on models for synthesis and translation.