Which method can enhance the performance of GANs?

Enhancing the performance of Generative Adversarial Networks (GANs) can be achieved through several methods that address training stability, model capacity, and generalization capabilities.

Increasing network depth allows the GANs to learn more complex representations and capture intricate details in the data. A deeper network can model more complex relationships, leading to improved image quality or more realistic outputs.

Adversarial training is the core process of how GANs function, where two networks—the generator and the discriminator—are trained jointly in a competitive setting. This process inherently pushes both networks to improve over iterations, enhancing the overall performance of the GAN.

Batch normalization is another technique that has proven effective in stabilizing the training process. It addresses issues like the internal covariate shift and helps in smoothing the loss landscape. By normalizing the inputs to each layer, batch normalization can lead to faster convergence and improved network performance.

Therefore, all these methods contribute significantly to enhancing the performance of GANs, making the correct answer encompass each of these approaches collectively.