Few-Shot Fast Adaptation Strategies with Meta-Learning and Multi-Armed Bandits

With the development of data-driven technologies, Few-Shot Learning (FSL) and environmental adaptability have become important research directions in machine learning. Traditional methods are highly dependent on large amounts of annotated data, which makes it difficult to cope with the data scarcity problem in the real world. This paper explores the integration of meta-learning and multi-armed bandit (MAB) algorithms in few-shot learning, aiming to investigate how their synergy improves model adaptability and decision-making efficiency in novel tasks. Through a review of existing literature, it analyzes the strengths of meta-learning in cross-task representation learning and the dynamic adaptability of MAB algorithms in uncertain environments. Their integration, supported by deep learning and attention mechanisms, is investigated in applications such as federated learning and remote sensing image analysis. The results demonstrate that meta-learning improves generalization by extracting cross-task knowledge, while MAB facilitates rapid task adaptation through effective exploration and exploitation strategies. Together, they form a unified framework that achieves state-of-the-art performance in few-shot learning across various domains.