Data Augmentation — artificially expanding the training dataset by applying random transformations, improving generalization without collecting more data.

Common Techniques (Vision)

• Geometric: Random crop, flip, rotation, scaling, affine transforms
• Color: Brightness, contrast, saturation, hue jitter
• Erasing: Random erasing, Cutout (mask random patches)
• Mixing: Mixup (blend two images + labels), CutMix (paste patches between images)
• Auto: AutoAugment, RandAugment — learned or random augmentation policies

NLP Augmentation

• Synonym replacement, random insertion/deletion
• Back-translation (translate to another language and back)
• Token masking (MLM-style)

Key Principles

• Augmentations should preserve the label (flipping a cat is still a cat)
• Stronger augmentation = more regularization but can hurt if too aggressive
• Test-Time Augmentation (TTA): Average predictions over augmented copies at inference for a small accuracy boost

Data augmentation is one of the simplest and most effective regularization techniques in deep learning.