The objective of a GAN is to train the generator to produce synthetic data samples that are indistinguishable from real data samples, while training the discriminator to correctly distinguish between real and fake samples. This is achieved through a minimax game, where the generator tries to maximize the probability of the discriminator making a mistake, while the discriminator tries to minimize this probability.
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