Exploring the Horizons of Meta-Learning in Neural Networks: A Survey of the State-of-the-Art

IF 5.3 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Asit Barman;Swalpa Kumar Roy;Swagatam Das;Paramartha Dutta
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引用次数: 0

Abstract

In the vast landscape of machine learning, meta-learning stands out as a challenging and dynamic area of exploration. While traditional machine learning models rely on standard algorithms to learn from data, meta-learning elevates this process by leveraging prior knowledge to adapt and improve learning experiences, mimicking the adaptive nature of human learning. This paradigm offers promising avenues for addressing the limitations of conventional deep learning approaches, such as data and computational constraints, as well as issues related to generalization. In this comprehensive survey, we delve into the intricacies of meta-learning methodologies. Beginning with a foundational overview of meta-learning and its associated fields, we present a detailed methodology elucidating the workings of meta-learning. Recognizing the importance of rigorous evaluation, we also furnish a comprehensive summary of prevalent benchmark datasets and recent advancements in meta-learning techniques applied to these datasets. Additionally, we explore meta-learning's diverse applications and achievements in domains like reinforcement learning and few-shot learning. Lastly, we examine practical hurdles and potential research directions, providing insights for future endeavors in this burgeoning field.
探索神经网络中元学习的视野:最新进展综述
在机器学习的广阔前景中,元学习作为一个具有挑战性和动态的探索领域脱颖而出。传统的机器学习模型依赖于标准算法从数据中学习,而元学习通过利用先验知识来适应和改进学习经验,模仿人类学习的适应性,从而提升了这一过程。这种范式为解决传统深度学习方法的局限性提供了有希望的途径,例如数据和计算约束,以及与泛化相关的问题。在这个全面的调查中,我们深入研究了元学习方法的复杂性。从元学习及其相关领域的基本概述开始,我们提出了一种详细的方法来阐明元学习的工作原理。认识到严格评估的重要性,我们还提供了普遍的基准数据集和应用于这些数据集的元学习技术的最新进展的全面总结。此外,我们还探讨了元学习在强化学习和少量学习等领域的各种应用和成就。最后,我们分析了现实障碍和潜在的研究方向,为这一新兴领域的未来努力提供了见解。
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来源期刊
CiteScore
10.30
自引率
7.50%
发文量
147
期刊介绍: The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.
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