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About this paper
Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi
A book chapter provides a comprehensive review of continual learning in artificial neural networks, detailing challenges such as catastrophic forgetting and outlining computational strategies to enable incremental learning. It clarifies problem scenarios and categorizes six main solution approaches, advocating for a holistic approach beyond merely preventing forgetting, emphasizing plasticity, positive transfer, and resource efficiency.
Key insights
- Continual learning requires more than just preventing catastrophic forgetting, also needing adaptation, positive transfer, task-agnosticism, noise tolerance, and resource efficiency.
- The field encompasses various scenarios, notably clarified into task-based vs. task-free learning and Task/Domain/Class-Incremental Learning, with Class-Incremental Learning posing significant challenges.
- Evaluation of continual learning methods needs to be comprehensive, including not just performance metrics but also diagnostic analyses and resource efficiency considerations.
Summary by alphaXiv.
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