What happens when the Discriminator becomes too strong?

When the Discriminator becomes too strong, it effectively means that it has become very good at distinguishing between real and generated (fake) data. In this scenario, the Generator, which is responsible for creating new data, faces a significant challenge. The strong Discriminator is able to detect even the slightest flaws or discrepancies in the Generator's outputs, making it harder for the Generator to produce realistic outputs that can fool the Discriminator.

This imbalance in the training process can lead to a situation where the Generator does not receive the constructive feedback it needs to improve. If the Discriminator consistently identifies the Generator's outputs as fake, the Generator lacks the incentive to evolve its techniques or learn, which ultimately hinders its ability to create more convincing and realistic data.

In contrast, if the Discriminator were not as strong, the Generator would have an easier time learning and refining its outputs, leading to more realistic results over time. This dynamic between the Generator and the Discriminator is crucial for the optimization of a Generative Adversarial Network (GAN), as it relies on both components engaging in a productive adversarial process.