  In this work, we propose to use out-of-distribution samples, i.e., unlabeled
samples coming from outside the target classes, to improve few-shot learning.
Specifically, we exploit the easily available out-of-distribution samples to
drive the classifier to avoid irrelevant features by maximizing the distance
from prototypes to out-of-distribution samples while minimizing that of
in-distribution samples (i.e., support, query data). Our approach is simple to
implement, agnostic to feature extractors, lightweight without any additional
cost for pre-training, and applicable to both inductive and transductive
settings. Extensive experiments on various standard benchmarks demonstrate that
the proposed method consistently improves the performance of pretrained
networks with different architectures.
