Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (3): 34-49.DOI: 10.3778/j.issn.1002-8331.2108-0213

• Research Hotspots and Reviews • Previous Articles     Next Articles

Research on Few-Shot Learning Based on Embedding Learning

HUANG Yanqian, CHI Dongxiang, XU Lingling   

  1. School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China
  • Online:2022-02-01 Published:2022-01-28



  1. 上海电机学院 电子信息学院,上海 201306

Abstract: In order to solve the huge challenge of machine learning in the case of small sample size, researchers put forward the concept of few-shot learning. Among the existing research work, the embedding learning has achieved good results and aroused a lot of attention. Based on this background, the method is classified into independent embedding model and hybrid embedding model according to the way of combining task characteristics when training embedding function. On the basis of the classification, the existing methods of embedding learning are studied. In addition, the existing standard data sets of few-shot learning are summarized, the performance of each class of embedding learning methods are illustrated, and the factors affecting the learning performance of few-shot learning are analyzed. Finally, the current challenges of feature embedding learning are discussed, and the directions of future research are also prospected.

Key words: machine learning, few-shot learning, feature embedding function, embedding learning

摘要: 为了解决机器学习在样本量较少的情况下所面临的巨大挑战,研究人员提出了小样本学习的概念。在现有的小样本学习研究工作中,嵌入学习方法取得了不错的效果,引发了大量关注。根据训练特征嵌入函数时结合任务特征信息的方式,将嵌入学习方法划分为单一嵌入模型和混合嵌入模型两大类。依据划分的类别,对现有的嵌入学习方法的研究工作展开进行研究。汇总了现有的小样本标准数据集,阐述了每一类嵌入学习方法的表现,分析了影响小样本学习性能的因素。讨论嵌入学习方法目前面临的挑战,并展望未来的研究方向。

关键词: 机器学习, 小样本学习, 特征嵌入函数, 嵌入学习方法