Why is data augmentation effectively free regularization?
easyAnswer
- Every augmentation is a prior about invariances the true function satisfies (translations of a cat are still a cat).
- Applying augmentations enlarges the effective training distribution without labeling cost.
- Forces the model to be invariant to those transformations, which reduces overfitting to specific instances.
- Free lunch in vision; needs task-specific care in text and tabular.
Check yourself — multiple choice
- Augmentation doubles training data at random
- Encodes invariance priors → larger effective distribution → reduces overfitting for free
- Same as label smoothing
- Only for classification
Augmentation = invariance prior → free effective data → regularization.
#augmentation#regularization
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