Journal
NEUROCOMPUTING
Volume 494, Issue -, Pages 203-223Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2022.04.078
Keywords
Meta-learning; Learning to learn; Application
Categories
Funding
- National Natural Science Foundation of China [12071458, 71731009]
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Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, which utilizes prior knowledge to assist the learning of new tasks, is an effective technique to overcome this issue.
Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable attention for its extraordinary performances. However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, known as learning to learn, is one of the effective techniques to overcome this issue. Meta-learning's generalization ability to unknown tasks is improved by employing prior knowledge to assist the learning of new tasks. There are mainly three types of meta-learning methods: metric-based, model-based, and optimization-based meta-learning. We investigate classical algorithms and recent meta-learning advances. Second, we survey meta-learning application in real world scenarios. Finally, we discuss present challenges and future research directions of meta-learning. CO 2022 Elsevier B.V. All rights reserved.
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