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    This study introduces a theoretical framework for lifelong learning (LLL) and a new dynamic expansion model (DEM) called the growing mixture model (GMM). The GMM efficiently learns new tasks while mitigating catastrophic forgetting using generative components.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Lifelong learning (LLL) aims for continuous knowledge acquisition without forgetting.
    • Dynamic expansion models (DEMs) are used to combat catastrophic forgetting in LLL.
    • Theoretical analysis of DEM efficiency in LLL is currently limited.

    Purpose of the Study:

    • To develop a theoretical framework for understanding forgetting in DEMs.
    • To introduce an efficient DEM, the growing mixture model (GMM), for LLL.
    • To enable efficient future task learning and parameter reduction.

    Main Methods:

    • Interpreting forgetting as statistical discrepancy distance.
    • Developing the growing mixture model (GMM) with a novel component selection mechanism.
    • Training a compact student model using GMM's generative samples.

    Main Results:

    • Theoretical analysis reveals a trade-off between model complexity and performance in mixture models.
    • The GMM efficiently adds generative components based on task novelty.
    • The student model significantly reduces parameters and enables fast inference.

    Conclusions:

    • The proposed theoretical framework provides insights into DEM efficiency.
    • The GMM offers an effective approach to lifelong learning and catastrophic forgetting.
    • The student model facilitates practical applications requiring reduced computational resources.