  Episodic learning is a popular practice among researchers and practitioners
interested in few-shot learning. It consists of organising training in a series
of learning problems, each relying on small "support" and "query" sets to mimic
the few-shot circumstances encountered during evaluation. In this paper, we
investigate the usefulness of episodic learning in Prototypical Networks and
Matching Networks, two of the most popular algorithms making use of this
practice. Surprisingly, in our experiments we found that, for Prototypical and
Matching Networks, it is detrimental to use the episodic learning strategy of
separating training samples between support and query set, as it is a
data-inefficient way to exploit training batches. These "non-episodic"
variants, which are closely related to the classic Neighbourhood Component
Analysis, reliably improve over their episodic counterparts in multiple
datasets, achieving an accuracy that (in the case of Prototypical Networks) is
competitive with the state-of-the-art, despite being extremely simple.
