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.

6 papers of 6,984Sort Recent · Most cited
  1. 2026
    Few-Shot Domain Incremental Learning via Continual Vision-Language ConsolidationNaeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer … Yew-Soon OngarXiv
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  2. 2025
    Continual Knowledge Consolidation LORA for Domain Incremental LearningNaeem Paeedeh, Mahardhika Pratama, Weiping Ding … Shiddiqi, AryarXiv
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  3. 2025
    CIBLS-PLS: A Class-Incremental Broad Learning System With Pseudolabel-Guided Stacked StructureXin Liu, Zhaoyin Shi, Shuanghao Zhang … C. L. Philip ChenIEEE TAI · Shenzhen University · Shenzhen Technology University · +4
  4. 2025
    Multi-View Fusion Graph Attention Network for Multilabel Class Incremental LearningAnhui Tan, Yu Wang, Wei-Zhi Wu … Jiye LiangInformation Fusion · Huaqiao University · Zhejiang Ocean University · +2
  5. 2023
    Assessor-Guided Learning for Continual EnvironmentsM. Anwar Ma’sum, Mahardhika Pratama, Edwin Lughofer … Wisnu JatmikoInformation Sciences
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  6. 2019
    Automatic Construction of Multi-layer Perceptron Network from Streaming ExamplesMahardhika Pratama, Choiru Za’in, Andri Ashfahani … Weiping DingCIKM · Nanyang Technological University · La Trobe University · +1
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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 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.