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In 2014, a machine-Understanding architecture often known as a generative adversarial network (GAN) was proposed by read more scientists at the College of Montreal. GANs use two styles that get the job done in tandem: A single learns to create a focus on output (like a picture) and the opposite learns to discriminate true facts in the generator’s output.
Assuming that your facts is usually converted into this normal, token format, then in idea, you could potentially use these ways to make new information that glance similar.
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