  Few-shot learning aims to build classifiers for new classes from a small
number of labeled examples and is commonly facilitated by access to examples
from a distinct set of 'base classes'. The difference in data distribution
between the test set (novel classes) and the base classes used to learn an
inductive bias often results in poor generalization on the novel classes. To
alleviate problems caused by the distribution shift, previous research has
explored the use of unlabeled examples from the novel classes, in addition to
labeled examples of the base classes, which is known as the transductive
setting. In this work, we show that, surprisingly, off-the-shelf
self-supervised learning outperforms transductive few-shot methods by 3.9% for
5-shot accuracy on miniImageNet without using any base class labels. This
motivates us to examine more carefully the role of features learned through
self-supervision in few-shot learning. Comprehensive experiments are conducted
to compare the transferability, robustness, efficiency, and the complementarity
of supervised and self-supervised features.
