  We consider a novel formulation of the problem of Active Few-Shot
Classification (AFSC) where the objective is to classify a small, initially
unlabeled, dataset given a very restrained labeling budget. This problem can be
seen as a rival paradigm to classical Transductive Few-Shot Classification
(TFSC), as both these approaches are applicable in similar conditions. We first
propose a methodology that combines statistical inference, and an original
two-tier active learning strategy that fits well into this framework. We then
adapt several standard vision benchmarks from the field of TFSC. Our
experiments show the potential benefits of AFSC can be substantial, with gains
in average weighted accuracy of up to 10% compared to state-of-the-art TFSC
methods for the same labeling budget. We believe this new paradigm could lead
to new developments and standards in data-scarce learning settings.
