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

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations