Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2026
    MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech RecognitionTheresa Pekarek Rosin, Matthias Kerzel, Stefan WermterarXiv · Universität Hamburg · Hamburg University of Technology
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  2. 2023
    Continual Robot Learning Using Self-Supervised Task InferenceMuhammad Burhan Hafez, Stefan WermterIEEE TCDS · Universität Hamburg · Hamburg University of Technology
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  3. 2023
    Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learningMuhammad Burhan Hafez, Tilman Immisch, Tom Weber, Stefan WermterFrontiers · Universität Hamburg · Hamburg University of Technology
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  4. 2023
    Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech RecognitionLeyuan Qu, Cornelius Weber, Stefan WermterNeural Networks · Universität Hamburg · Zhejiang Lab · +2
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  5. 2023
    Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech RecognitionTheresa Pekarek Rosin, Stefan WermterSpringer LNCS · Universität Hamburg · Hamburg University of Technology
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  6. 2021
    Behavior Self-Organization Supports Task Inference for Continual Robot LearningMuhammad Burhan Hafez, Stefan WermterIROS · Universität Hamburg · Hamburg University of Technology
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  7. 2018
    An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial ExpressionsHenrique Siqueira, Pablo Barros, Sven Magg, Stefan WermterIROS · Universität Hamburg · Hamburg University of Technology
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  8. 2017
    Lifelong learning of human actions with deep neural network self-organizationGerman I. Parisi, Jun Tani, Cornelius Weber, Stefan WermterNeural Networks · Universität Hamburg · Hamburg University of Technology · +1
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.