Meta learning Strategies for Few shot Learning

Abstract

Meta-learning, or learning to learn, has gained significant attention in the machine learning community as a promising approach to enable models to learn from a limited number of examples, known as few-shot learning. Few-shot learning is critical in scenarios where acquiring large amounts of labeled data is challenging or expensive. Meta-learning strategies aim to address this challenge by leveraging prior knowledge from similar tasks to quickly adapt to new tasks with limited data. This paper provides a comprehensive overview of meta- learning strategies for few-shot learning, including model-agnostic meta-learning (MAML), gradient-based meta-learning, and metric-based meta-learning approaches. We discuss the key concepts, methodologies, and challenges in meta-learning for few-shot learning, and present a comparative analysis of state-of-the-art techniques. Additionally, we explore the applications and future directions of meta-learning in addressing the challenges of few-shot learning.