Data Augmentation

Techniques for artificially expanding a training dataset by creating modified versions of existing data, such as rotations, translations or paraphrasing, to improve model robustness.

Creating more training data by modifying existing data. Flipping images, adding noise, paraphrasing text; it's like multiplying your dataset to make the AI more robust without collecting new data.