The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. 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.

翻译：预训练模型的泛化能力是少样本深度学习的关键。Dropout是一种在传统深度学习方法中使用的正则化技术。本文探索了Dropout在少样本学习中的威力，并提供了关于如何利用它的一些见解。在少样本目标检测和少样本图像分类数据集（即Pascal VOC、MS COCO、CUB和mini-ImageNet）上进行的大量实验验证了我们的方法的有效性。