Explore the Power of Dropout on Few-shot Learning
It addresses improving few-shot learning performance for tasks like object detection and image classification, but appears incremental as it applies an existing technique to a specific domain.
The paper investigates using dropout regularization to improve generalization in few-shot learning, showing effectiveness on object detection and image classification datasets like Pascal VOC, MS COCO, CUB, and mini-ImageNet.
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.