Data Augmentation

Data augmentation creates modified or synthetic training examples so models can learn from limited, imbalanced, or varied data. Common approaches include geometric and appearance transforms, Mixup and CutMix, graph and feature perturbations, and examples generated with diffusion models, language models, or simulation. Recent work increasingly adapts augmentation to a model’s learning state and to domain-specific constraints in settings such as medical imaging, speech, and robotics. The central tension is to add useful diversity without changing labels or important structure; effectiveness depends on the task and should be tested for out-of-domain performance, calibration, and synthetic-data quality as well as accuracy.