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Continual learning and catastrophic forgetting

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

7 Citations (Scopus)

Abstract

This chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a nonstationary 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 chapter reviews the insights that this field has generated.

Original languageEnglish
Title of host publicationLearning and Memory
Subtitle of host publicationA Comprehensive Reference
EditorsJ Wixted
PublisherAcademic Press
PagesV1:153-V1:168
EditionThird
ISBN (Electronic)9780443157547
ISBN (Print)9780443157554
DOIs
Publication statusPublished - 2025
Externally publishedYes

Keywords

  • Catastrophic forgetting
  • Cognitive science
  • Context-dependent processing
  • Continual learning
  • Deep learning
  • Deep neural networks
  • Functional regularization
  • Incremental learning
  • Lifelong learning
  • Optimization-based approaches
  • Parameter regularization
  • Replay
  • Template-based classification

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