Two networks playing a forgery game taught computers to create
orig. “Generative Adversarial Nets” · Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Long before modern image generators, this paper had two networks compete, and the result was machines that could make new pictures from scratch.
The setup is a game between two networks. One, the generator, tries to make fake images. The other, the discriminator, tries to tell fakes from real photos. As each gets better, the generator is pushed to make more convincing images, until its output starts to look real. This was one of the first methods that could generate believable new images instead of just sorting existing ones.
This kicked off the wave of AI that creates rather than classifies. The adversarial idea showed up in art tools, photo editing, and data generation for years. Newer methods like diffusion have taken over for images, but the question it asked, how do you teach a machine to make something new, is still central.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, University of Montreal
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