What are some effective data augmentation techniques for AI?

What are some effective data augmentation techniques for AI?

Data augmentation techniques include rotation, flipping, scaling, and adding noise to images. These help improve model robustness.

Can you explain how adding noise works?

Adding noise involves introducing random variations to the data, which helps the model generalize better by preventing overfitting.

Are there specific techniques for text data augmentation?

Yes, techniques for text include synonym replacement, random insertion, and back-translation to create diverse training samples.

How do these techniques impact model performance?

They can significantly enhance model performance by providing more diverse training data, leading to better generalization.