Reviewing Meta-Learning: Approaches, Algorithms, and Applications

Abstract

Meta-learning, also known as learning to learn, has garnered significant attention for enabling models to acquire knowledge and adapt to new tasks with limited data. This review paper surveys meta-learning approaches, including model-agnostic meta-learning (MAML), metric-based methods, and memory-augmented architectures. It discusses the applications of meta-learning in few-shot learning, reinforcement learning, and optimization, along with challenges and open research questions.