  We consider the problem of semi-supervised few-shot classification where a
classifier needs to adapt to new tasks using a few labeled examples and
(potentially many) unlabeled examples. We propose a clustering approach to the
problem. The features extracted with Prototypical Networks are clustered using
$K$-means with the few labeled examples guiding the clustering process. We note
that in many real-world applications the adaptation performance can be
significantly improved by requesting the few labels through user feedback. We
demonstrate good performance of the active adaptation strategy using image
data.
