Generative adversarial network (GAN)
A generative adversarial network is a pair of neural networks trained against each other to create realistic data such as images. One network, the generator, produces fakes; the other, the discriminator, tries to tell fakes from real examples. As each improves, the generator's output becomes harder to distinguish from the real thing.
Why it comes up
GANs came out of Bengio's Montreal lab in 2014, in a paper led by his student Ian Goodfellow, and were the leading method for generating images until diffusion models took over around 2021.
Yoshua Bengio