All glossary terms
techniquesintermediate

Data Augmentation

Techniques that artificially expand training data by applying transformations while preserving labels.

In more detail

Data augmentation includes rotations, flips, crops for images; back-translation for text; noise addition; mixup; etc. Increases data diversity, reduces overfitting, and improves generalization without collecting new data.

Examples

  • Image rotation, flipping, cropping
  • Back-translation for NLP
  • Mixup and cutmix
  • Noise injection

Related terms

RegularizationOverfitting
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