What are effective strategies to mitigate racial and gender bias in GANs generating human faces

Question

With the help of python programming tell me What are effective strategies to mitigate racial and gender bias in GANs generating human faces?

Answer 1

strategies to mitigate racial and gender bias in GANs generating human faces are as follows:

• Diverse Training Data: Include balanced representation of races, genders, and ethnicities.
• Bias Regularization: Apply fairness constraints or adversarial training during training.
• Data Augmentation: Enhance dataset diversity with techniques like rotation and cropping.
• Bias Detection: Use fairness metrics to evaluate and adjust outputs regularly.
• Post-Processing: Filter generated faces to meet fairness criteria.

Here is the code snippet you can refer to:

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

• Diverse Data: Balance the dataset across races, genders, and ethnicities.
• Bias Detection: Regularly evaluate and adjust for fairness.
• Post-Processing: Apply fairness filters to generated faces.