How do you handle imbalanced datasets when training or fine-tuning generative models especially with class distribution biases

Question

Can you tell me how can i handle imbalanced datasets when training or fine-tuning generative models use python programming to show?

Answer 1

You can easily handle imbalanced datasets when training or fine-tuning generative models by referring to following code:

In the above code techniques like Class Weights and SMOTE were implemented to modify loss to handle imbalance and oversampling the minority class to balance dataset.