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In 2014, a machine-Understanding architecture called a generative adversarial network (GAN) was proposed by researchers within the University of Montreal. GANs use two products that get the job done in tandem: A person learns to crank out a concentrate on output (like an image) and the other learns to discriminate genuine knowledge from your generator’s output.
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