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Continual learning: A systematic literature review.

Qinwen Yang1, Liyuan Wang2, Joerg Wicker1

  • 1School of Computer Science, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand.

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|November 3, 2025
PubMed
Summary
This summary is machine-generated.

Intelligent systems need continual learning (CL) to adapt without forgetting. This review analyzes 189 papers (2010-2025), offering new insights into catastrophic forgetting and future research directions in CL.

Keywords:
Catastrophic forgettingContinual learningIncremental learningLifelong learningSystematic literature review

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

Background:

  • Intelligent systems require continuous adaptation to evolving data streams.
  • Continual learning (CL) enables systems to learn sequentially without prior data.
  • Catastrophic forgetting (CF) is a major challenge, where new learning impairs old knowledge.

Purpose of the Study:

  • To provide a systematic literature review (SLR) of continual learning (CL) research from 2010 to 2025.
  • To address limitations in existing surveys by unifying classifications and exploring causes of catastrophic forgetting (CF).
  • To advance a theoretical framework for CL challenges beyond empirical testing.

Main Methods:

  • Conducted automated searches across five major databases.
  • Performed manual searches across 13 key academic venues.
  • Curated a dataset of 189 peer-reviewed papers on continual learning.

Main Results:

  • Presented a comprehensive overview of CL research trends and methodologies.
  • Developed refined taxonomies for CL methods and experimental settings.
  • Analyzed structural and functional causes of catastrophic forgetting (CF).
  • Identified open theoretical questions and emerging research directions in CL.

Conclusions:

  • The review offers a unified perspective on CL, crucial for developing resilient AI.
  • Understanding CF causes is vital for advancing CL methods.
  • The findings guide future research towards more robust and adaptable intelligent systems.