  In few-shot classification, we are interested in learning algorithms that
train a classifier from only a handful of labeled examples. Recent progress in
few-shot classification has featured meta-learning, in which a parameterized
model for a learning algorithm is defined and trained on episodes representing
different classification problems, each with a small labeled training set and
its corresponding test set. In this work, we advance this few-shot
classification paradigm towards a scenario where unlabeled examples are also
available within each episode. We consider two situations: one where all
unlabeled examples are assumed to belong to the same set of classes as the
labeled examples of the episode, as well as the more challenging situation
where examples from other distractor classes are also provided. To address this
paradigm, we propose novel extensions of Prototypical Networks (Snell et al.,
2017) that are augmented with the ability to use unlabeled examples when
producing prototypes. These models are trained in an end-to-end way on
episodes, to learn to leverage the unlabeled examples successfully. We evaluate
these methods on versions of the Omniglot and miniImageNet benchmarks, adapted
to this new framework augmented with unlabeled examples. We also propose a new
split of ImageNet, consisting of a large set of classes, with a hierarchical
structure. Our experiments confirm that our Prototypical Networks can learn to
improve their predictions due to unlabeled examples, much like a
semi-supervised algorithm would.
