Compared with collecting and labelling additional real-world examples, what is the principal advantage that data augmentation offers?
- AIt increases training diversity cheaply by transforming data already on hand, avoiding the cost and time of gathering and annotating new samples. Correct
- BIt guarantees that the augmented samples introduce entirely new real-world scenarios absent from the original collected data.
- CIt removes the need for any regularisation, because augmented data alone fully eliminates overfitting in every model.
- DIt produces a smaller model with fewer parameters, because augmented inputs let the network discard redundant layers during training.
Why A is correct: Correct: augmentation reuses existing labelled data through transformations, expanding diversity at low cost and effort relative to sourcing and annotating new real examples.
Why B is wrong: Tempting because augmentation does add variety, but it derives variants from existing samples and cannot introduce genuinely novel phenomena that were never captured; only new real data can do that.
Why C is wrong: Tempting because augmentation does reduce overfitting, but it complements rather than replaces other regularisers and does not eliminate overfitting universally; the absolute claim is wrong.
Why D is wrong: Tempting because efficiency is desirable, but augmentation affects the data fed to the model, not the model's parameter count or architecture; it does not shrink the network.