How can I prevent overfitting in a generative model trained on limited data

Question

With the help of code, can you tell me how I can prevent overfitting in a generative model trained on limited data?

Answer 1

To prevent overfitting in a generative model trained on limited data, you can use techniques like data augmentation, regularization (e.g., dropout, weight decay), and early stopping.

Here is the code reference you can refer to:

In the above code, we are using the following:

• Data Augmentation: Increase data diversity (e.g., flipping, cropping) to help the model generalize better.
• Regularization (Dropout): Use dropout (Dropout(0.5)) to prevent overfitting by randomly deactivating neurons during training.
• Early Stopping: Stop training early when the validation loss stops improving to prevent overfitting on limited data.

Hence, by referring to the above, you can prevent overfitting in a generative model trained on limited data.