  The field of few-shot learning has made remarkable strides in developing
powerful models that can operate in the small data regime. Nearly all of these
methods assume every unlabeled instance encountered will belong to a handful of
known classes for which one has examples. This can be problematic for
real-world use cases where one routinely finds 'none-of-the-above' examples. In
this paper we describe this challenge of identifying what we term
'out-of-support' (OOS) examples. We describe how this problem is subtly
different from out-of-distribution detection and describe a new method of
identifying OOS examples within the Prototypical Networks framework using a
fixed point which we call the generic representation. We show that our method
outperforms other existing approaches in the literature as well as other
approaches that we propose in this paper. Finally, we investigate how the use
of such a generic point affects the geometry of a model's feature space.
