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Table of Contents
1. Introduction
2. Background
2.1 Formalizing Meta-Learning
2.2 Historical Context of Meta-Learning
2.3 Related Fields
Summary
The field of meta-learning, or learning-to-learn, has gained significant interest in recent years. Meta-learning aims to improve the learning algorithm itself based on the experience of multiple learning episodes. This survey provides insights into the contemporary meta-learning landscape, discussing definitions, applications, and challenges. It explores the potential of meta-learning in addressing data and computation bottlenecks, generalization, and improving learning efficiency. The paper also presents a new taxonomy for meta-learning methods and highlights promising applications such as few-shot learning and reinforcement learning. Overall, it offers a comprehensive overview of neural network meta-learning, focusing on the advancement of deep learning industry and addressing key criticisms.