Meta-Learning and Few-Shot Learning: A Comprehensive Survey of Algorithms, Theory, and Applications

Meta-learning, commonly referred to as "learning to learn," has emerged as a transformative paradigm in machine learning, enabling models to rapidly adapt to new tasks with minimal training data. This comprehensive survey presents a systematic analysis of meta-learning and few-shot learning approaches, examining their theoretical foundations, algorithmic innovations, and practical applications across diverse domains. We categorize existing methods into three primary paradigms: optimizationbased approaches that learn initialization parameters, metricbased methods that learn embedding spaces for effective comparison, and model-based architectures that incorporate rapid adaptation mechanisms. Through extensive analysis of recent advances, we identify key challenges including task distribution shift, computational efficiency, and theoretical understanding of generalization bounds. Our survey reveals that while significant progress has been made in benchmark performance, practical deployment remains constrained by factors such as meta-overfitting, task diversity requirements, and computational overhead. We provide a unified framework for understanding different metalearning approaches and their relationships, analyze empirical results across standard benchmarks, and outline promising research directions including integration with foundation models, continual meta-learning, and applications in data-scarce domains. This survey serves as both a comprehensive reference for researchers and a practical guide for practitioners implementing meta-learning systems.