Why does my GAN model output blurry images despite using a deep discriminator

Question

With the help of a good coding example, can you explain why my GAN model outputs blurry images despite using a deep discriminator?

Answer 1

Blurry images in GAN outputs often result from the following issues:

• Mode Collapse
  • The generator learns to produce limited variations, leading to blurry outputs.
  • Solution: Use techniques like feature matching or Wasserstein GAN (WGAN).
• Insufficient Generator Depth
  • A shallow generator struggles to capture fine details.
  • Solution: Increase the generator's depth and use skip connections (e.g., ResNet blocks).
• Imbalanced Training
  • A deep discriminator may overpower the generator, stalling its learning.
  • Solution: Update the generator more frequently or balance loss scaling.
• Poor Loss Function
  • Standard GAN loss may not incentivize sharp outputs.
  • Solution: Use perceptual loss or hinge loss.
• Training Instability
  • Training instability can lead to suboptimal convergence.
  • Solution: Use spectral normalization for stable training.

Here are the code snippets you can refer to:

Hence, systematically addressing these factors can help improve the sharpness of GAN-generated images.