Meta-Learning in Neural Networks: A Survey.

Area of Science:

• Artificial Intelligence
• Machine Learning

Background:

• Meta-learning, or learning-to-learn, is gaining prominence in AI.
• It contrasts with traditional AI by improving learning algorithms through experience, not just solving tasks from scratch.
• This paradigm offers solutions to deep learning limitations such as data and computation constraints, and generalization issues.

Purpose of the Study:

• To provide a comprehensive survey of the current meta-learning landscape.
• To define meta-learning and differentiate it from related fields like transfer learning and hyperparameter optimization.
• To introduce a novel taxonomy for categorizing meta-learning methods.

Main Methods:

• Literature review and synthesis of contemporary meta-learning research.
• Development of a new classification system (taxonomy) for meta-learning approaches.
• Analysis of successful applications and emerging trends in meta-learning.

Main Results:

• A structured overview of meta-learning definitions and its relation to other AI fields.
• A proposed taxonomy offering a detailed breakdown of current meta-learning methodologies.
• Identification of key application areas, including few-shot learning and reinforcement learning.

Conclusions:

• Meta-learning presents a powerful paradigm for advancing AI, particularly in overcoming deep learning bottlenecks.
• The proposed taxonomy aids in understanding the diverse landscape of meta-learning methods.
• Future research directions are highlighted, focusing on addressing outstanding challenges and exploring new opportunities.