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Few-shot learning aims to build classifiers for new classes from a smallnumber of labeled examples and is commonly facilitated by access to examplesfrom a distinct set of 'base classes'. The difference in data distributionbetween the test set (novel classes) and the base classes used to learn aninductive bias often results in poor generalization on the novel classes. Toalleviate problems caused by the distribution shift, previous research hasexplored the use of unlabeled examples from the novel classes, in addition tolabeled examples of the base classes, which is known as the transductivesetting. In this work, we show that, surprisingly, off-the-shelfself-supervised learning outperforms transductive few-shot methods by 3.9% for5-shot accuracy on miniImageNet without using any base class labels. Thismotivates us to examine more carefully the role of features learned throughself-supervision in few-shot learning. Comprehensive experiments are conductedto compare the transferability, robustness, efficiency, and the complementarityof supervised and self-supervised features.

Shot in the Dark: Few-Shot Learning with No Base-Class Labels