How should samples in real datasets be utilized in Generative Adversarial Networks (GAN)?

In the context of Generative Adversarial Networks (GANs), real datasets play a crucial role in informing the training process of the models involved. The correct choice indicates that real samples from the dataset should be utilized as the input value of the discriminative model.

The discriminative model, also known as the discriminator, is responsible for distinguishing between real samples from the dataset and synthetic samples generated by the generative model. It is crucial for the discriminator to have access to real data samples so it can learn the features and characteristics that define genuine data distributions. By providing these real samples, the discriminator can evaluate the quality of the generated samples and give feedback to the generator to improve its outputs.

This feedback loop is essential as it enables the generator to progressively enhance its ability to produce realistic samples that can fool the discriminator. The effectiveness of the GAN architecture relies heavily on this adversarial training process, making the use of real datasets vital for the discriminator's function.

The other options do not align with the roles of the generative and discriminative models within the GAN framework, as the generator creates synthetic outputs and does not directly utilize real samples as input.