How do you prevent discrepancy in real-time model output during adversarial training in GANs

Question

With the help of code can you explain How do you prevent discrepancy in real-time model output during adversarial training in GANs?

Answer 1

Prevent discrepancy in real-time GAN training by stabilizing adversarial learning with gradient penalty, adaptive learning rates, and historical averaging.Here is the code snippet you can refer to:

In the above code, we are using the following key approaches

• Gradient Penalty (GP): Regularizes discriminator gradients to prevent mode collapse.

• Adaptive Learning Rates: Uses Adam with tuned betas for stable convergence.

• Balanced Adversarial Updates: Prevents one model from dominating the other.

• Historical Averaging Ready: Can be extended with past model states to smooth training.

Hence, by applying gradient penalty and adaptive optimization, adversarial training remains stable, reducing discrepancies in real-time GAN outputs.