摘要
小样本学习旨在让机器像人类一样通过对少量样本的学习达到对事物认知和概括的能力.基于度量的小样本学习方法希望学习一个低维嵌入空间,直接对比查询集合和支持类之间的相似性,分类测试样本.文中针对基于度量的小样本学习方法,尝试从这类方法需要解决的关键问题、类表示学习和相似性度量入手,梳理相关文献.与已有相关综述不同,文中只针对基于度量的小样本学习方法进行更详尽全面的分类,而且从关键问题角度进行分类.最后总结目前代表性工作在常用的图像分类任务数据集上的实验结果,分析现有方法存在的问题,并展望未来工作.
Few-shot learning aims to make machines recognize and summarize things by learning from a small number of samples like humans.The metric-based few-shot learning method is designed to learn a low-dimensional embedding space and query samples can be classified based on a distance between the query samples and the class embeddings in this space.In this paper,the key issues,class representation learning and similarity learning,are discussed to sort out the relevant literature.Only metric-based few-shot learning methods are classified in a detailed and comprehensive way,and they are classified from the perspective of key issues.Finally,the experimental results of current representative research on commonly used image classification datasets are summarized,the problems of the existing methods are analyzed,and the future research is prospected.
作者
刘鑫
周凯锐
何玉琳
景丽萍
于剑
LIU Xin;ZHOU Kairui;He Yulin;JING Liping;YU Jian(Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044;School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044;Beijing Newlink Technology Co.,Ltd.,Beijing 100083)
出处
《模式识别与人工智能》
CSCD
北大核心
2021年第10期909-923,共15页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61632004)
基本科研业务费研究生创新项目(No.2021YJS031)
北京市自然科学基金项目(No.Z180006)
中央高校基本科研业务费专项项目(No.2019JBZ110)资助。
关键词
小样本学习
基于度量的小样本学习
类表示
相似性学习
图像分类
Few-Shot Learning
Metric-Based Few-Shot Learning
Class Representation
Similarity Learning
Image Classification
