Abstract
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.
Abstract (translated)
预训练模型的泛化能力是少量深度学习的关键。 dropout 是传统深度学习方法中常用的正则化技术。在本文中,我们将探讨 dropout 在少量学习中的应用及其应用方式。我们对少量物体检测和少量图像分类数据集(如Pascal VOC、MS COCO、鳄鱼和迷你 ImageNet)进行了广泛的实验,以验证我们的方法的有效性。
URL
https://arxiv.org/abs/2301.11015
