Which term describes the situation when the Discriminator achieves high accuracy very early in training?

The situation where the Discriminator achieves high accuracy very early in the training process is best described as overfitting. Overfitting occurs when a model learns to memorize the training data, capturing noise and fluctuations rather than the underlying distribution.

In the context of a Generative Adversarial Network (GAN), if the Discriminator is overly successful at distinguishing between real and generated samples early on, it means it has possibly fitted too tightly to the training data. As a result, it may fail to generalize well, which can lead to a decrease in its ability to correctly identify generated samples as training continues. This early overfitting can be detrimental in the GAN framework, as the balance between the Generator and Discriminator is essential for effective training; if the Discriminator too quickly becomes too powerful, the Generator may struggle to improve.

This contrasts with the other terms. The catalytic effect typically refers to instances where initial improvements in one model can boost the performance of another model. Early convergence describes a scenario where a model quickly reaches a stable solution, but it might not indicate that the model has learned appropriately. Gradient blowup involves excessively large gradients during training, leading to instability. Understanding these distinctions is key in the context of training GANs effectively.