  Few-shot classification aims at classifying categories of a novel task by
learning from just a few (typically, 1 to 5) labelled examples. An effective
approach to few-shot classification involves a prior model trained on a
large-sample base domain, which is then finetuned over the novel few-shot task
to yield generalizable representations. However, task-specific finetuning is
prone to overfitting due to the lack of enough training examples. To alleviate
this issue, we propose a new finetuning approach based on contrastive learning
that reuses unlabelled examples from the base domain in the form of
distractors. Unlike the nature of unlabelled data used in prior works,
distractors belong to classes that do not overlap with the novel categories. We
demonstrate for the first time that inclusion of such distractors can
significantly boost few-shot generalization. Our technical novelty includes a
stochastic pairing of examples sharing the same category in the few-shot task
and a weighting term that controls the relative influence of task-specific
negatives and distractors. An important aspect of our finetuning objective is
that it is agnostic to distractor labels and hence applicable to various base
domain settings. Compared to state-of-the-art approaches, our method shows
accuracy gains of up to $12\%$ in cross-domain and up to $5\%$ in unsupervised
prior-learning settings.
