The first idea, not new to GANs, is to use randomness as an ingredient. The idea of a machine "creating" realistic images from scratch can seem like magic, but GANs use two key tricks to turn a vague, seemingly impossible goal into reality. Besides the intrinsic intellectual challenge, this turns out to be a surprisingly handy tool, with applications ranging from art to enhancing blurry images. You might wonder why we want a system that produces realistic images, or plausible simulations of any other kind of data. A generative adversarial network (GAN) is an especially effective type of generative model, introduced only a few years ago, which has been a subject of intense interest in the machine learning community. By contrast, the goal of a generative model is something like the opposite: take a small piece of input-perhaps a few random numbers-and produce a complex output, like an image of a realistic-looking face. Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, "cat").
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