Knowledge-aware Zero-Shot Learning: Survey and Perspective
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4366-4373.
https://doi.org/10.24963/ijcai.2021/597
Keywords
- Knowledge representation and reasoning: General
- Multidisciplinary topics and applications: General
- Machine learning: General
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.
@inproceedings{ijcai2021p597,
title = {Knowledge-aware Zero-Shot Learning: Survey and Perspective},
author = {Chen, Jiaoyan and Geng, Yuxia and Chen, Zhuo and Horrocks, Ian and Pan, Jeff Z. and Chen, Huajun},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {4366--4373},
year = {2021},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2021/597},
url = {https://doi.org/10.24963/ijcai.2021/597},
}
TY - CPAPER TI - Knowledge-aware Zero-Shot Learning: Survey and Perspective AU - Chen, Jiaoyan AU - Geng, Yuxia AU - Chen, Zhuo AU - Horrocks, Ian AU - Pan, Jeff Z. AU - Chen, Huajun T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 4366 EP - 4373 DO - 10.24963/ijcai.2021/597 UR - https://doi.org/10.24963/ijcai.2021/597 ER -Copied
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