![]() ![]() Meanwhile, the discriminator will compare the output of the generator with the original dataset, and in the process train its counterpart to produce content close to the source material. The generator is trained on a sample dataset (the highway) and then told to produce content based on what it saw. GameGAN is a generative adversarial network created by Nvidia that learns to visibly imitate a desired game by ingesting screenplay and keyboard actions during training.Įvery GAN consists of two competing networks - a generator and a discriminator. We didn't have much knowledge, so we had a lot of learning to do, and still do." "It was a lot of trial and error and just small tweaks and seeing if and how they improved. Neither of the two collaborators came in with much knowledge of GANs, Kinsley told Eurogamer.
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