How do I handle non-converging loss curves in generative adversarial models

Question

Can you tell me how I handle non-converging loss curves in generative adversarial models?

Answer 1

To handle non-converging loss curves in generative adversarial models (GANs), you can follow the following steps:

• Adjust Learning Rates: Reduce learning rates for both generator and discriminator to avoid instability.
• Use Label Smoothing: Apply label smoothing to the discriminator's real labels to prevent overfitting.
• Use Adaptive Optimizers: Switch to optimizers like RMSprop or Adam with different hyperparameters.
• Use Gradient Clipping: Clip gradients to prevent exploding gradients.
• Regularization: Apply techniques like Dropout or Weight Regularization to the discriminator to prevent overfitting.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Learning Rate Adjustment: Use smaller learning rates to prevent drastic updates.
• Label Smoothing: Smooth real labels to avoid overfitting the discriminator and ensure smoother loss curves.
• Gradient Clipping: Clip gradients if necessary to prevent exploding gradients.

Hence, these strategies can help stabilize the training process and guide the GAN towards convergence.