GANs are a type of machine learning algorithm that involves framing a problem as a supervised learning problem with two sub-models. The AI model is trained to create a new set of data points belonging to a particular domain. In contrast, the classifier model, known as the discriminator, identifies the new set of data points as either real or fake. In this kind of repetitive training, the generator takes the chance of generating closer-to-reality examples, while the discriminator becomes wiser in determining fake and real samples.
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