  We consider a practical scenario of machine unlearning to erase a target
dataset, which causes unexpected behavior from the trained model. The target
dataset is often assumed to be fully identifiable in a standard unlearning
scenario. Such a flawless identification, however, is almost impossible if the
training dataset is inaccessible at the time of unlearning. Unlike previous
approaches requiring a complete set of targets, we consider few-shot unlearning
scenario when only a few samples of target data are available. To this end, we
formulate the few-shot unlearning problem specifying intentions behind the
unlearning request (e.g., purely unlearning, mislabel correction, privacy
protection), and we devise a straightforward framework that (i) retrieves a
proxy of the training data via model inversion fully exploiting information
available in the context of unlearning; (ii) adjusts the proxy according to the
unlearning intention; and (iii) updates the model with the adjusted proxy. We
demonstrate that our method using only a subset of target data can outperform
the state-of-the-art unlearning methods even with a complete indication of
target data.
