  Conventional training of deep neural networks usually requires a substantial
amount of data with expensive human annotations. In this paper, we utilize the
idea of meta-learning to explain two very different streams of few-shot
learning, i.e., the episodic meta-learning-based and pre-train finetune-based
few-shot learning, and form a unified meta-learning framework. In order to
improve the generalization power of our framework, we propose a simple yet
effective strategy named meta-dropout, which is applied to the transferable
knowledge generalized from base categories to novel categories. The proposed
strategy can effectively prevent neural units from co-adapting excessively in
the meta-training stage. Extensive experiments on the few-shot object detection
and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and
mini-ImageNet, validate the effectiveness of our method.
