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In 2014, a device-learning architecture often known as a generative adversarial network (GAN) was proposed by researchers in the University of Montreal. GANs use two types that work in tandem: A single learns to crank out a concentrate on output (like a picture) and the opposite learns to discriminate true information from your generator’s output