Knowledge-aware Zero-Shot Learning: Survey and Perspective
Knowledge-aware Zero-Shot Learning: Survey and Perspective
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DOI：
10.24963/ijcai.2021/597
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发表时间：
2021-02
影响因子：
2.1
通讯作者：
Jiaoyan Chen;Yuxia Geng;Zhuo Chen;Ian Horrocks;Jeff Z. Pan;Huajun Chen
中科院分区：
文献类型：
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作者：
Jiaoyan Chen;Yuxia Geng;Zhuo Chen;Ian Horrocks;Jeff Z. Pan;Huajun Chen
零射击学习（Zero-shot learning， ZSL）是一种利用外部知识（即侧信息）预测训练过程中从未出现过的类的学习方法，已经得到了广泛的研究。本文从外部知识的角度对ZSL进行了文献综述，对外部知识进行了分类，回顾了他们的方法，并对不同的外部知识进行了比较。通过文献综述，我们进一步讨论和展望了符号知识在解决ZSL和其他机器学习样本短缺问题中的作用。
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.