Ian Goodfellow et al. introduced generative adversarial networks, opening an era of realistic image synthesis.
In 2014, Ian Goodfellow and coauthors proposed Generative Adversarial Networks: a generator and discriminator trained in a minimax. The generator learns to fool; the discriminator, to spot fakes.
GANs produced increasingly credible images (DCGAN, StyleGAN) and fueled debates on deepfakes and synthetic creativity.
Later, diffusion and generative transformers displaced GANs on many benchmarks, but the adversarial frame remains influential in robustness and evaluation.
OpenAI presented GPT-2 and initially limited release of the largest model over disinformation risks, foreshadowing open vs closed dilemmas.
Tomas Mikolov and Google colleagues released Word2Vec, popularizing dense embeddings and king–man+woman≈queen analogies.
He et al. introduced residual networks that enabled training hundreds of layers and won ILSVRC 2015.