How does data augmentation differ for images and text?

How does data augmentation differ for images and text?

Data augmentation for images often involves techniques like rotation, flipping, and scaling, while for text, it includes synonym replacement, back-translation, and random insertion.

Can you give examples of specific techniques used for text data augmentation?

Sure! Techniques for text data augmentation include synonym replacement, where words are replaced with their synonyms, and back-translation, where text is translated to another language and then back to the original.

What are the benefits of using data augmentation?

Data augmentation helps improve model robustness, increases the diversity of training data, and can reduce overfitting by providing more varied examples.

Are there any downsides to data augmentation?

Yes, potential downsides include the risk of introducing noise into the data and the increased computational cost during training.