In GANs, what is the primary objective of the discriminator network?

In Generative Adversarial Networks (GANs), the primary objective of the discriminator network is to distinguish between real data samples and fake data samples generated by the generator network. The discriminator acts as a binary classifier, learning to assign a probability to each input, indicating whether it is real (from the training dataset) or fake (created by the generator). The discriminator is trained to maximize its ability to correctly classify real and fake samples. For example, if the GAN is trained to generate images of cats, the discriminator would be trained to identify real images of cats from a dataset and distinguish them from images of cats generated by the generator. The better the discriminator becomes at distinguishing real from fake, the more challenging it becomes for the generator to produce realistic samples. This adversarial process, where the generator tries to fool the discriminator and the discriminator tries to not be fooled, drives both networks to improve, leading to the generator producing increasingly realistic data samples. Therefore, the discriminator's objective is crucial for the GAN to learn to generate high-quality data.