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 7,070Sort Recent · Most cited
  1. 2023
    A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual LearningNaoki Masuyama, Takanori Takebayashi, Yusuke Nojima … Stefan WermterNeural Computing and Applications · Osaka Prefecture University · University of Malaya · +2
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  2. 2023
    Read Between the Layers: Leveraging Intra-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained ModelsKyra Ahrens, Hans Hergen Lehmann, Jae Hee Lee, Stefan WermterTrans. Mach. Learn. Res.
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  3. 2023
    Continual Robot Learning Using Self-Supervised Task InferenceMuhammad Burhan Hafez, Stefan WermterIEEE TCDS · Universität Hamburg · Hamburg University of Technology
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  4. 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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  5. 2023
    Explainable Lifelong Stream Learning Based on "Glocal" Pairwise FusionChu Kiong Loo, Wei Shiung Liew, Stefan WermterarXiv
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  6. 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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  7. 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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  8. 2023
    Visually Grounded Continual Language Learning with Selective SpecializationKyra Ahrens, Lennart Bengtson, Jae Yeol Lee, Stefan WermterEMNLP · Universität Hamburg
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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 written 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.