Can data augmentation improve model performance?

How can data augmentation help improve the performance of my machine learning model?

Data augmentation can enhance model performance by increasing the diversity of your training dataset, helping the model generalize better.

What are some common techniques for data augmentation?

Common techniques include image rotation, flipping, scaling, cropping, and adding noise. For text, you can use synonym replacement or back-translation.

Are there any downsides to using data augmentation?

Yes, if not done carefully, it can introduce noise or unrealistic examples that may confuse the model, leading to worse performance.

How do I know if data augmentation is working for my model?

You can evaluate model performance on a validation set before and after applying data augmentation to see if there's an improvement.