基于深度学习的少样本研究综述

Researches on Few-shot Learning Based on Deep Learning:an Overview

基于大数据的深度学习算法越来越完善,然而如何解决训练样本数非常少的情况,是目前神经网络研究领域中一个非常重要且极具挑战的问题。首先,介绍了少样本问题的定义;接着将现有的少样本学习方法分为数据增强、度量学习和元学习三类,分别从方法所用模型、数据集以及相应的实验结果进行分析;最后,总结了现有方法的不足,探讨了未来少样本研究的方向。

Deep learning algorithms based on big data are becoming more and more perfect.However,how to solve the situation where the number of training samples is very small is a very important and challenging problem in the existing neural network research field.Firstly,the definition of few-shot learning is introduced.Then,the existing few-shot learning methods are divided into three categories,data enhancement,metric learning and meta-learning,which are analyzed and discussed in terms of the models used in the method,data sets and corresponding experimental results.Finally,the shortcomings of the existing methods are summarized,and the direction of future few-shot learning research is discussed.