How do I solve class imbalance during image generation tasks in GANs

Question

With the help of Python programming, can you explain how I solve class imbalance during image generation tasks in GANs?

Answer 1

To solve class imbalance during image generation tasks in GANs, you can follow the following steps:

• Class-Conditional GANs (cGANs): Incorporate class labels into the generator and discriminator.
• Weighted Loss Functions: Assign higher weights to underrepresented classes in the discriminator's loss.
• Oversampling: Generate more samples for underrepresented classes.
• Data Augmentation: Augment samples from underrepresented classes.

Here is the code snippet you can refer to:

In the above code, we are using the following steps:

• Class Conditioning: Embed class labels and combine them with input noise or image.
• Weighted Loss: Use class weights in the discriminator loss​
• Oversampling: Generate more samples for minority classes to balance the dataset.

Hence, this approach ensures GANs handle class imbalance effectively during training and generation.