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Continual Learning and Catastrophic Forgetting

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arxiv 2403.05175 v1 pith:CMXRPNHW submitted 2024-03-08 cs.LG cs.AIcs.CVq-bio.NCstat.ML

Gido M. van de Ven , Nicholas Soures , Dhireesha Kudithipudi This is my paper · ORCID
classification cs.LGcs.AIcs.CVq-bio.NCstat.ML
keywords learningcontinualbookcatastrophicchapterforgettingnetworksalthough
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This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for artificial neural networks. An important reason is that, when learning something new, these networks tend to quickly and drastically forget what they had learned before, a phenomenon known as catastrophic forgetting. Especially in the last decade, continual learning has become an extensively studied topic in deep learning. This book chapter reviews the insights that this field has generated.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning

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  5. Quo Vadis, World Modeling?

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  6. Gated Adaptation for Continual Learning in Human Activity Recognition

    cs.LG 2026-03 conditional novelty 5.0 of 10

    Channel-wise gating of frozen pretrained features reduces catastrophic forgetting in subject-incremental HAR, reaching ~78% final accuracy on PAMAP2 versus ~57% for full fine-tuning.

  7. Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Neuron-Fusion selectively restores large-change neurons from a fine-tuned multimodal model and suppresses small changes, improving language retention with modest visual trade-offs.

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