Meta-Learning: Adaptive and Fast Learning Systems

Abstract

Meta-learning has emerged as a powerful paradigm in machine learning, enabling adaptive and fast learning systems capable of efficiently acquiring knowledge from various tasks and domains. This paper provides an overview of meta-learning techniques, focusing on their ability to leverage prior experience to facilitate the learning of new tasks. We explore the fundamental concepts, methodologies, and applications of meta-learning, emphasizing its role in enhancing the adaptability and speed of learning systems. By incorporating meta-learning strategies, algorithms can autonomously adapt to new tasks and data distributions, thereby improving performance and efficiency across diverse domains. This review sheds light on the current state-of-the-art in meta-learning research and highlights its potential implications for the future of artificial intelligence.