How do I address data imbalance in generative models for text and image generation tasks

Question

With the help of Python programming, can you explain How do I address data imbalance in generative models for text and image generation tasks?

Answer 1

In order to address data imbalance in generative models for text and image generation tasks, you can refer to the following steps below:

• Augmentation for Minority Classes
  • Use data augmentation techniques to increase the representation of underrepresented classes.
• Weighted Loss Functions
  • Assign higher weights to minority classes in the loss function.
• Balanced Sampling
  • Oversampling or undersampling can be used to balance the dataset before training.
• Class-Specific Generative Models
  • Train separate models or use techniques like GANs with class-conditioning to generate data for minority classes.
• Synthetic Data Generation
  • Generate synthetic text or images for the minority class using models like GANs or VAEs.

Here is the code snippet you can refer to, showing the above steps:

Hence, By combining these methods, you can mitigate data imbalance and improve the quality of generative models.