What challenge is commonly faced when training GANs?

The challenge commonly faced when training Generative Adversarial Networks (GANs) is that of imbalance, which can lead to either the generator or discriminator dominating the training process. This oscillation can significantly undermine the training dynamics and the overall effectiveness of the GAN.

In GANs, the generator aims to produce realistic data samples to fool the discriminator, which is tasked with distinguishing between real and generated data. If the discriminator becomes too powerful too quickly, it can easily label all the generated samples as fake, providing little feedback for the generator to improve. Conversely, if the generator outpaces the discriminator, it can produce samples that consistently fool the discriminator but lack real-world relevance or diversity, leading to issues like mode collapse where the generator only produces a limited variety of outputs.

Therefore, maintaining a balance between the generator and discriminator during training is crucial for success. This challenge underscores the dynamic nature of GAN training, where both components must evolve together to achieve a stable and effective learning process.