Paper Title
UNDERSTANDING THE STATE OF THE ART: UNVEILING META-LEARNING PERSPECTIVES

Abstract
Meta-learning, a subfield of machine learning, has garnered significant attention in recent years due to its potential to enable systems to learn and adapt dynamically across diverse tasks and domains. This work provides an extensive analysis and critical overview of meta-learning approaches and their applications, as reported in current research. By means of a thorough analysis of several research publications, we explore the theoretical underpinnings, algorithmic developments, and real-world applications of meta-learning techniques. Numerous applications are covered by our survey, such as few-shot learning, transfer learning, reinforcement learning, and optimization, among others. We also go over the potential, problems, and future directions in the topic of meta-learning. This critical review fosters further advancements in meta-learning research and its practical consequences across diverse domains by synthesizing existing information and identifying important areas for future investigation. Keywords - Meta-learning, transfer learning, few-shot learning, meta-learning algorithms, computer vision, natural language processing, federated learning, MAML