# Explore the Power of Dropout on Few-shot Learning

Source: [Explore the Power of Dropout on Few-shot Learning](<https://www.sota2.com/research/paper/68fa6e761e209131cea40030>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:20:27.891Z

Authors: Shaobo Lin, Xingyu Zeng, Rui Zhao

Published: 2023-01-26T10:10:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.11015>)

[PDF](<https://arxiv.org/pdf/2301.11015.pdf>)