Exploring the Horizons of Meta-Learning in Neural Networks: A Survey of the State-of-the-Art

In the vast landscape of machine learning, meta-learning stands out as a challenging and dynamic area of exploration. While traditional machine learning models rely on standard algorithms to learn from data, meta-learning elevates this process by leveraging prior knowledge to adapt and improve learning experiences, mimicking the adaptive nature of human learning. This paradigm offers promising avenues for addressing the limitations of conventional deep learning approaches, such as data and computational constraints, as well as issues related to generalization. In this comprehensive survey, we delve into the intricacies of meta-learning methodologies. Beginning with a foundational overview of meta-learning and its associated fields, we present a detailed methodology elucidating the workings of meta-learning. Recognizing the importance of rigorous evaluation, we also furnish a comprehensive summary of prevalent benchmark datasets and recent advancements in meta-learning techniques applied to these datasets. Additionally, we explore meta-learning's diverse applications and achievements in domains like reinforcement learning and few-shot learning. Lastly, we examine practical hurdles and potential research directions, providing insights for future endeavors in this burgeoning field.