Which technique is often employed to improve training stability in GANs?

Gradient penalty methods are widely recognized for promoting stability during the training of Generative Adversarial Networks (GANs). This technique works by adding a regularization term to the loss function that penalizes the gradient norm of the discriminator. Specifically, when the gradient of the discriminator's output with regard to its input is taken, the gradient penalty ensures that this norm remains close to one. This addresses issues related to the Lipschitz continuity of the discriminator, which is essential for achieving a stable equilibrium between the generator and discriminator.

The use of gradient penalty methods helps prevent the generator from producing unrealistic outputs while simultaneously keeping the discriminator from becoming excessively overpowered. By enforcing constraints on the gradients, this method reduces the likelihood of oscillations and mode collapse during training, both of which are common challenges in training GANs. Overall, incorporating gradient penalties leads to a more stable training environment, facilitating the effective convergence of the GAN's components.

While data normalization, regularization techniques, and batch size reduction can also contribute to model training, they do not specifically address the unique challenges presented in GAN training to the same extent as gradient penalty methods.