Data Augmentation

When training examples are scarce, data augmentation creates useful variations without collecting an entirely new dataset. An image may be cropped, rotated, recolored, or flipped while retaining its original label; audio can be shifted or mixed with noise; and text can be paraphrased or altered under carefully controlled rules. These transformations expose a model to greater diversity, reduce memorization, and often improve performance on unfamiliar inputs. The technique must preserve the meaning relevant to the task, since an unrealistic change can introduce incorrect labels or harmful artifacts. Effective augmentation reflects conditions the system is likely to encounter after deployment and is evaluated separately from the untouched validation and test data used to measure whether generalization actually improved.