Survey on Zero-Shot Learning

doi:10.3778/j.issn.1002-8331.2106-0133

Abstract

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.

Key words: zero-shot learning, attribute, embedding space, general model

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个关键问题以及现有的解决方案，给出文本、图像、视频三方面常用的数据集；按照关键技术（属性、嵌入以及生成模型）出现时间顺序，对13个典型模型如何进行零样本学习展开描述，并对优点、缺点、创新点、挑选数据集以及表现进行总结；从词、图像、视频3个维度详细介绍了零样本学习在各个领域的应用；提出了零样本学习过程中出现的挑战并给出了对应的潜在研究方向。

关键词: 零样本学习, 属性, 嵌入空间, 生成模型

WANG Zeshen，YANG Yun，XIANG Hongxin, LIU Qing. Survey on Zero-Shot Learning[J]. Computer Engineering and Applications, 2021, 57(19): 1-17.

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.

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