Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Meta-learning, often described as “learning to learn,” is an emerging paradigm in artificial intelligence that focuses on enabling models to adapt quickly to new tasks with minimal data and training. Unlike traditional machine learning approaches that learn a single task from scratch, meta-learning aims to extract transferable knowledge from a variety of tasks so that learning new tasks becomes faster and more efficient. This paper presents a comprehensive review of meta-learning techniques, including metric-based, model-based, and optimization-based approaches. It also explores the relationship between meta-learning and few-shot learning, transfer learning, and continual learning. Various real-world applications such as robotics, healthcare, natural language processing, and computer vision are discussed. The paper highlights current challenges and future research directions, including scalability, interpretability, and integration with reinforcement learning. Meta-learning shows promise in creating adaptable and intelligent systems capable of generalization beyond conventional training methods.

Keywords: Meta-learning, Few-shot learning, Transfer learning, Optimization-based learning, Metric learning, Adaptation, Artificial Intelligence