How can I solve class imbalance issues when training a generative model in FastAI

Question

With the help of Python programming, can you tell me How I can solve class imbalance issues when training a generative model in FastAI?

Answer 1

To solve class imbalance issues when training a generative model in FastAI, techniques like class weighting, oversampling, or undersampling should be used to adjust the training data distribution.

Here is the code reference you can refer to:

In the above code, we are using the following:

• Class Weighting: Compute class weights using compute_class_weight and apply it to the loss function to give higher importance to underrepresented classes.
• Data Augmentation: You could also use oversampling or undersampling techniques to modify the dataset distribution (e.g., using RandomSampler).

Hence, by referring to the above, you can solve class imbalance issues when training a generative model in FastAI