Data Augmentation

Data augmentation is commonly used in scenarios with limited labeled data to expose the model to greater variety. Examples include image rotation, cropping, noise injection, paraphrasing text, or applying distortions to audio. The aim is not to create entirely new knowledge, but to present plausible variations of the existing data. This can improve generalization and reduce overfitting risk. However, augmentation methods must be appropriate to the problem context; otherwise they may create unrealistic or misleading examples.