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

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively.
• 2 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.
• 3 Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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