What common issue do GANs face related to training dynamics?

Generative Adversarial Networks (GANs) are particularly well-known for the instability in their training dynamics. This instability often arises from the adversarial setup where two neural networks, the generator and the discriminator, are trained simultaneously. The generator aims to create realistic data, while the discriminator strives to distinguish between real and synthetic data.

This competitive nature can lead to several training challenges. For instance, if the generator improves too quickly compared to the discriminator, it may produce outputs that the discriminator cannot accurately judge. Conversely, if the discriminator becomes too powerful, it can effectively impede the generator's ability to improve, leading to scenarios where one network's performance overshadows the other's. This can cause fluctuations in the loss values, making it hard to converge on an optimal solution, resulting in poor model performance.

The other choices highlight distinct problems within machine learning, but they do not encapsulate the primary concern found in GAN training. Overfitting pertains to a model performing well on training data but poorly on unseen data, which is less prominent in the adversarial context of GANs compared to traditional models. While GANs can experience long training times and issues with generalization, the hallmark problem linked to their unique architecture is the instability that arises during training, which