  Few-shot learning (FSL) methods typically assume clean support sets with
accurately labeled samples when training on novel classes. This assumption can
often be unrealistic: support sets, no matter how small, can still include
mislabeled samples. Robustness to label noise is therefore essential for FSL
methods to be practical, but this problem surprisingly remains largely
unexplored. To address mislabeled samples in FSL settings, we make several
technical contributions. (1) We offer simple, yet effective, feature
aggregation methods, improving the prototypes used by ProtoNet, a popular FSL
technique. (2) We describe a novel Transformer model for Noisy Few-Shot
Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to
weigh mislabeled versus correct samples. (3) Finally, we extensively test these
methods on noisy versions of MiniImageNet and TieredImageNet. Our results show
that TraNFS is on-par with leading FSL methods on clean support sets, yet
outperforms them, by far, in the presence of label noise.
