Has anyone implemented a custom loss function for a GAN with improved results

Question

For my GAN project, which aims to generate high-quality images, how can I approach developing a custom loss function? Can you suggest using code?

Answer 1

When creating a custom loss function for your GAN project to generate high-quality photos, here are some procedures you can refer:

Identify the limitations of the standard loss function: Begin by investigating the issues using common loss functions such as Binary Cross-Entropy or Wasserstein loss. For example, they may cause mode collapse or insufficient detail in generated images.

Define Your Objectives: Determine which characteristics of the created photographs you wish to improve. This could involve improving image detail, decreasing artifacts, or encouraging diversity in generated samples.

• Explore Custom Loss Variants: Consider alternative approaches, such as:
• Perceptual Loss: Use a pre-trained model (such as VGG) to extract features and calculate loss using perceptual differences rather than pixel differences. This focuses on the quality of the generated photographs.

Combine Losses: You can also use numerous loss functions to balance different elements. For example, you could mix perceptual and adversarial loss:

Using these five steps, you can implement a custom loss function for your GAN project with improved results.