Proceedings

Knowledge-aware Zero-Shot Learning: Survey and Perspective

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4366-4373.

https://doi.org/10.24963/ijcai.2021/597

Abstract

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.

Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, and Huajun Chen. 2021. Knowledge-aware Zero-Shot Learning: Survey and Perspective. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4366–4373. https://doi.org/10.24963/ijcai.2021/597
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