Data Augmentation

Data augmentation is one of the most effective regularization tools in deep learning, especially when labeled data is limited. Rotations, crops, color perturbations, noise injection, or semantics-preserving text transformations may be applied. This helps the model become more robust to minor variations in the data. Because representation learning is so data-dependent, augmentation can be as important as the model architecture itself.