A Comprehensive Survey of Continual Learning: Theory, Method and Application

🤖 Plain-English Summary

To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. Then we provide a advanced and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications.

🔑 Key Findings

• This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively.
• In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks.
• Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning.

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