  Few-Shot Learning (FSL) algorithms have made substantial progress in learning
novel concepts with just a handful of labelled data. To classify query
instances from novel classes encountered at test-time, they only require a
support set composed of a few labelled samples. FSL benchmarks commonly assume
that those queries come from the same distribution as instances in the support
set. However, in a realistic set-ting, data distribution is plausibly subject
to change, a situation referred to as Distribution Shift (DS). The present work
addresses the new and challenging problem of Few-Shot Learning under
Support/Query Shift (FSQS) i.e., when support and query instances are sampled
from related but different distributions. Our contributions are the following.
First, we release a testbed for FSQS, including datasets, relevant baselines
and a protocol for a rigorous and reproducible evaluation. Second, we observe
that well-established FSL algorithms unsurprisingly suffer from a considerable
drop in accuracy when facing FSQS, stressing the significance of our study.
Finally, we show that transductive algorithms can limit the inopportune effect
of DS. In particular, we study both the role of Batch-Normalization and Optimal
Transport (OT) in aligning distributions, bridging Unsupervised Domain
Adaptation with FSL. This results in a new method that efficiently combines OT
with the celebrated Prototypical Networks. We bring compelling experiments
demonstrating the advantage of our method. Our work opens an exciting line of
research by providing a testbed and strong baselines. Our code is available at
https://github.com/ebennequin/meta-domain-shift.
