Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtentionJuncheng Liao, Jinfan Lv, Guoming Wang … Siliang TangarXiv
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  2. 2024
    E-CGL: an efficient continual graph learnerJianhao Guo, Zixuan Ni, Yun Zhu, Siliang TangFrontiers of Information Technology & Electronic Engineering · Zhejiang University of Science and Technology
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  3. 2023PDF ↗
  4. 2023
    On Representation-Level Forgetting in Class Incremental Learning: What's the Bottleneck?Zixuan Ni, Haizhou Shi, Longhui Wei … Siliang TangSocial Science Research Network · First Affiliated Hospital Zhejiang University · Zhejiang University
  5. 2021
    Self-Supervised Class Incremental LearningZixuan Ni, Siliang Tang, Yueting ZhuangarXiv
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  6. 2021
    Revisiting Catastrophic Forgetting in Class Incremental LearningZixuan Ni, Haizhou Shi, Siliang Tang … Zhuang, YuetingarXiv · Zhejiang University
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  7. 2021
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 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.