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    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.