A "discriminator" is a component commonly used in machine learning, particularly in the context of generative adversarial networks (GANs). Its primary function is to distinguish between real data and data generated by a model. By evaluating the authenticity of inputs, the discriminator helps improve the quality of generated outputs over time. In a typical GAN setup, the discriminator works alongside a generator. While the generator creates new data samples, the discriminator assesses them against real samples from a training dataset. This adversarial process encourages the generator to produce increasingly realistic data, enhancing the overall performance of the model.