  Few-shot learning requires to recognize novel classes with scarce labeled
data. Prototypical network is useful in existing researches, however, training
on narrow-size distribution of scarce data usually tends to get biased
prototypes. In this paper, we figure out two key influencing factors of the
process: the intra-class bias and the cross-class bias. We then propose a
simple yet effective approach for prototype rectification in transductive
setting. The approach utilizes label propagation to diminish the intra-class
bias and feature shifting to diminish the cross-class bias. We also conduct
theoretical analysis to derive its rationality as well as the lower bound of
the performance. Effectiveness is shown on three few-shot benchmarks. Notably,
our approach achieves state-of-the-art performance on both miniImageNet (70.31%
on 1-shot and 81.89% on 5-shot) and tieredImageNet (78.74% on 1-shot and 86.92%
on 5-shot).
