A Comprehensive Review of Meta-Learning: From Theoretical Foundations to Practical Applications

Meta-learning, often characterized as "learning to learn," addresses a fundamental limitation of conventional deep learning: the requirement for large amounts of labeled data to achieve good performance on new tasks. By training models on a distribution of related tasks rather than a single task, meta-learning enables rapid adaptation to novel tasks using only a handful of examples. This paradigm has gained significant attention due to its potential to reduce data requirements and build more flexible, adaptive AI systems. This survey provides a comprehensive treatment of modern neural meta-learning, emphasizing both theoretical foundations and practical applications. We formalize meta-learning as a bilevel optimization problem and present a detailed taxonomy of approaches, including metric-based methods that learn embedding spaces, model-based methods that internalize adaptation in network architectures, and optimization-based methods that learn initializations or update rules. We examine connections between meta-learning and related paradigms such as transfer learning, multi-task learning, and continual learning, clarifying the distinctive characteristics of each. We dedicate substantial coverage to applications across diverse domains including computer vision (few-shot classification, object detection, domain adaptation), reinforcement learning (meta-RL algorithms and robotics), natural language processing (few-shot text classification, low-resource translation, in-context learning), and specialized areas such as drug discovery, medical imaging, and federated learning. Beyond surveying existing methods, we provide critical analysis of current challenges including meta-overfitting, computational efficiency, and evaluation protocols. We discuss theoretical developments in understanding meta-generalization through PAC-Bayes bounds and information-theoretic analyses, and identify promising research directions for advancing toward more data-efficient and adaptable artificial intelligence systems.