  Few-shot learning aims to classify unseen classes with only a limited number
of labeled data. Recent works have demonstrated that training models with a
simple transfer learning strategy can achieve competitive results in few-shot
classification. Although excelling at distinguishing training data, these
models are not well generalized to unseen data, probably due to insufficient
feature representations on evaluation. To tackle this issue, we propose
Semantically Proportional Patchmix (SePPMix), in which patches are cut and
pasted among training images and the ground truth labels are mixed
proportionally to the semantic information of the patches. In this way, we can
improve the generalization ability of the model by regional dropout effect
without introducing severe label noise. To learn more robust representations of
data, we further take rotate transformation on the mixed images and predict
rotations as a rule-based regularizer. Extensive experiments on prevalent
few-shot benchmarks have shown the effectiveness of our proposed method.
