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零样本学习综述
引用本文:王泽深,杨云,向鸿鑫,柳青. 零样本学习综述[J]. 计算机工程与应用, 2021, 57(19): 1-17. DOI: 10.3778/j.issn.1002-8331.2106-0133
作者姓名:王泽深  杨云  向鸿鑫  柳青
作者单位:1.云南大学 软件学院,昆明 650504 2.云南省数据科学与智能计算重点实验室,昆明 650504
摘    要:尽管自深度学习发展以来,减少大量人工标记样本的需求使得零样本学习取得了不错的进展,以至于已经拥有比较完善的理论体系.但是对于零样本学习应用的研究寥寥无几,如何有效地对应用领域进行梳理是现阶段急需解决的问题.对零样本的理论体系进行介绍,通过一个例子引出零样本学习的定义,继而与广义零样本、监督学习比较,再而列举4个关键问题...

关 键 词:零样本学习  属性  嵌入空间  生成模型

Survey on Zero-Shot Learning
WANG Zeshen,YANG Yun,XIANG Hongxin,LIU Qing. Survey on Zero-Shot Learning[J]. Computer Engineering and Applications, 2021, 57(19): 1-17. DOI: 10.3778/j.issn.1002-8331.2106-0133
Authors:WANG Zeshen  YANG Yun  XIANG Hongxin  LIU Qing
Affiliation:1.School of Software, Yunnan University, Kunming 650504, China 2.Yunnan Key Laboratory of Data Science and Intelligent Computing, Kunming 650504, China
Abstract:Although there have been well developed in zero-shot learning since the development of deep learning, in the aspect of the application, zero-shot learning did not have a good system to order it. This paper overviews theoretical systems of zero-shot learning, typical models, application systems, present challenges and future research directions. Firstly, it introduces the theoretical systems from definition of zero-shot learning, essential problems, and commonly used data sets. Secondly, some typical models of zero-shot learning are described in chronological order. Thirdly, it presents the application systems about of zero-shot learning from the three dimensions, such as words, images and videos. Finally, the paper analyzes the challenges and future research directions in zero-shot learning.
Keywords:zero-shot learning  attribute  embedding space  general model  
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