  Few-shot learning aims to classify unseen classes with a few training
examples. While recent works have shown that standard mini-batch training with
a carefully designed training strategy can improve generalization ability for
unseen classes, well-known problems in deep networks such as memorizing
training statistics have been less explored for few-shot learning. To tackle
this issue, we propose self-augmentation that consolidates self-mix and
self-distillation. Specifically, we exploit a regional dropout technique called
self-mix, in which a patch of an image is substituted into other values in the
same image. Then, we employ a backbone network that has auxiliary branches with
its own classifier to enforce knowledge sharing. Lastly, we present a local
representation learner to further exploit a few training examples for unseen
classes. Experimental results show that the proposed method outperforms the
state-of-the-art methods for prevalent few-shot benchmarks and improves the
generalization ability.
