Data Augmentation

/ˈdeɪtə ˌɔːɡmɛnˈteɪʃən/

Techniques to artificially increase training data size by creating modified versions of existing data.

Data augmentation creates variations of training examples through transformations like rotation, flipping, cropping (images), synonym replacement (text), or noise injection (audio). Improves generalization and reduces overfitting.