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.

9 papers of 8,653Sort Recent · Most cited
  1. 2025
    L-GraK: Learning Long-Term Grasping Knowledge With Feature Map Distillation in Robotic GraspingJiajie Wen, Dongjiang Li, Jing Yang … Jing LuoInternational Conference on Advanced Robotics and Mechatr… · Shanxi University · Central China Normal University · +1
  2. 2024
    Confidence Self-Calibration for Multi-Label Class-Incremental LearningKaile Du, Yifan Zhou, Fan Lyu … Guangcan LiuECCV
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  3. 2020
    Enhancing network modularity to mitigate catastrophic forgettingLu Chen, Masayuki MurataApplied Network Science · Kyoto Institute of Technology · Osaka Health Science University · +1
  4. 2020
    Mitigate Catastrophic Forgetting by Varying GoalsLu Chen, Masayuki MurataInternational Conference on Agents and Artificial Intelli… · Kyoto Institute of Technology · The University of Osaka
  5. 2018
  6. 2018
    Mitigate catastrophic forgetting for continuously learning linked open data using modularityLu Chen, Masayuki MurataInternational Conference on Innovation in Artificial Inte… · The University of Osaka
  7. 2018
    Alleviating Catastrophic Forgetting with Modularity for Continuously Learning Linked Open DataLu Chen, Masayuki MurataInternational Journal of Computer Theory and Engineering · The University of Osaka
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  8. 2017
  9. 2017
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. By default it shows the 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. The rest are one click away under “All papers”. 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.