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2. Meta-Learning

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Wenwu Zhu
Xin Wang
Published in
Automated Machine Learning and Meta-Learning for Multimedia
Publisher
Springer International Publishing

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Abstract

Meta-learning, a paradigm that mimics human learning by enabling models to learn from few data, has gained significant attention in recent years. This chapter explores the fundamental concepts and advanced techniques in meta-learning, defining it from two perspectives: task distribution view and learner and meta-learner view. It discusses model-based methods that use specialized models for rapid learning, metric-based methods that transfer knowledge through similar tasks, and optimization-based methods that improve optimization performance across tasks. The chapter also highlights recent advances such as dynamic meta-learning, multimodal meta-learning, meta reinforcement learning, and meta-learning for graphs, showcasing the versatility and potential of meta-learning in various applications. By providing a detailed examination of these approaches, the chapter offers insights into the future directions of meta-learning research and its practical implications.
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previous chapter Automated Machine Learning
next chapter Automated Machine Learning for Multimedia
Title
Meta-Learning
Authors
Wenwu Zhu
Xin Wang
Copyright Year
2021
Book
Automated Machine Learning and Meta-Learning for Multimedia

Print ISBN: 978-3-030-88131-3

Electronic ISBN: 978-3-030-88132-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-88132-0_2
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