  Few-shot learning and self-supervised learning address different facets of
the same problem: how to train a model with little or no labeled data. Few-shot
learning aims for optimization methods and models that can learn efficiently to
recognize patterns in the low data regime. Self-supervised learning focuses
instead on unlabeled data and looks into it for the supervisory signal to feed
high capacity deep neural networks. In this work we exploit the complementarity
of these two domains and propose an approach for improving few-shot learning
through self-supervision. We use self-supervision as an auxiliary task in a
few-shot learning pipeline, enabling feature extractors to learn richer and
more transferable visual representations while still using few annotated
samples. Through self-supervision, our approach can be naturally extended
towards using diverse unlabeled data from other datasets in the few-shot
setting. We report consistent improvements across an array of architectures,
datasets and self-supervision techniques.
