Definition
Data augmentation expands or varies a training set by transforming existing examples or generating new ones while intending to preserve task-relevant meaning. Examples include cropping or rotating images, perturbing audio, paraphrasing text, generating counterexamples, and simulating rare conditions.
Distinguish it from nearby terms
Synthetic data can be created independently from simulations or generative systems; augmentation begins with a training objective and adds controlled variation. A transformation is harmful when it changes the correct label, erases important minority cases, or amplifies artifacts.
Check your understanding
Validate augmented data against the invariance being assumed. More examples do not help when the transformation teaches the wrong task.