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“There are dissimilarities in how these models function And the way we expect the human brain will work, but I think You will also find similarities.
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In 2014, a machine-learning architecture called a generative adversarial network (GAN) was proposed by researchers within the College of Montreal. GANs use two products that operate in tandem: One learns to create a target output (like a picture) and the opposite learns to discriminate legitimate knowledge through the generator’s output.
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