What would you do if your GAN produces low-quality outputs despite fine-tuning the model for real-world data

Question

Can you tell me What you would do if your GAN produces low-quality outputs despite fine-tuning the model for real-world data?

Answer 1

If a GAN produces low-quality outputs despite fine-tuning, you can follow the following:

• Review Data Preprocessing: Ensure proper normalization and augmentation of real-world data.
• Enhance Model Architecture: Use advanced layers like residual blocks or attention mechanisms.
• Adjust Training Dynamics: Balance generator and discriminator updates to avoid overfitting or underfitting.
• Regularization: Add gradient penalties (e.g., WGAN-GP) or noise to stabilize training.
• Increase Training Data Quality/Quantity: Include more diverse and high-quality samples.

Here is the code snippet you can refer to:

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

• Gradient Penalty: Stabilizes training by penalizing non-smooth discriminator gradients.
• Architecture Improvements: Helps the GAN learn more robust representations.
• Data Preprocessing: Ensures the data aligns well with the model's expectations.

Hence, these methods collectively address low-quality outputs and refine the GAN's performance.