Transfer learning is sometimes split into domain adaptation and task transfer. Which statement most accurately contrasts these two situations?
- ADomain adaptation keeps the same task but shifts the input distribution, while task transfer keeps related inputs but targets a different task, often requiring a new output head. Correct
- BDomain adaptation always requires a brand-new output head, while task transfer never changes the output head because the label set is unchanged.
- CDomain adaptation and task transfer both assume the input distribution and the label space are identical, differing only in the size of the training set used.
- DTask transfer changes the input domain while keeping the same labels, whereas domain adaptation changes the labels while keeping the same input domain.
Why A is correct: This is correct: domain adaptation addresses a change in input distribution for the same task, whereas task transfer reuses learned representations for a different target task, which typically needs a new output head for the new label space.
Why B is wrong: This inverts the typical pattern: a changed task usually needs a new head because the label space differs, whereas a domain shift with the same task often reuses the head. The absolute always and never also overstate the rule, so this is wrong.
Why C is wrong: This is tempting as a tidy unification, but it erases the very distinction: if both input distribution and label space matched, there would be no domain shift or task change to speak of, so the premise is incorrect.
Why D is wrong: This is a plausible-sounding swap of the definitions, but it reverses them: a changed input distribution with the same task is domain adaptation, and a changed task is task transfer, so the mapping here is backwards.