What are the challenges of data augmentation in AI training?

What are the challenges of data augmentation in AI training?

Data augmentation can introduce noise, lead to overfitting, and may not generalize well across different datasets.

Can you give examples of common data augmentation techniques?

Sure! Common techniques include rotation, flipping, scaling, and adding noise to images.

How do these techniques affect model performance?

They can improve model robustness and generalization, but excessive augmentation might degrade performance.

What strategies can mitigate the challenges of data augmentation?

Using a validation set to monitor performance, applying augmentation selectively, and combining techniques can help.