Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

16 papers of 11,817Sort Recent · Most cited
  1. 2026
  2. 2026
    Parameter Merging with Gradient-Guided Supermasks in Online Continual LearningBenliu Qiu, Heqian Qiu, Lanxiao Wang … Hongliang LiAAAI
  3. 2025
    D3Net: Dual-Path Decoupling-Distillation for Adaptive Fusion in Continual Egocentric LearningChenghao Qi, Heqian Qiu, Zhaofeng Shi … Hongliang LiIEEE International Workshop on Multimedia Signal Processing
  4. 2025
    Geodesic-Aligned Gradient Projection for Continual Task LearningBenliu Qiu, Heqian Qiu, Haitao Wen … Hongliang LiTIP
  5. 2025
    Class Incremental Learning With Less Forgetting Direction and Equilibrium PointHaitao Wen, Heqian Qiu, Lanxiao Wang … Hongliang LiIEEE TCSVT
  6. 2025
    Adaptively forget with crossmodal and textual distillation for class-incremental video captioningHuiyu Xiong, Lanxiao Wang, Heqian Qiu … Hongliang LiNeurocomputing
  7. 2024
    Video Class-Incremental Learning With Clip Based TransformerShuyun Lu, Jian Jiao, Lanxiao Wang … Hongliang LiInternational Conference on Information Photonics
  8. 2024
    Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding ConfusionShaoxu Cheng, Kanglei Geng, Chi-Yuan He … Hongliang LiIEEE Trans. Multimedia
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  9. 2024
    Must Unsupervised Continual Learning Relies on Previous Information?Haoyang Cheng, Haitao Wen, Heqian Qiu … Hongliang LiCVPR
  10. 2024
    Class Incremental Learning with Multi-Teacher DistillationHaitao Wen, Li-Li Pan, Yu Dai … Hongliang LiCVPR
  11. 2024PDF ↗
  12. 2023
  13. 2023
  14. 2023
  15. 2023
    Optimizing Mode Connectivity for Class Incremental LearningHaitao Wen, Haoyang Cheng, Heqian Qiu … Hongliang LiICML
  16. 2023
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.