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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    From Storage to Experience: A Survey on the Evolution of LLM Agent Memory MechanismsJinghao Luo, Yuchen Tian, Chuxue Cao … Jing MaACL · South China Normal University · Hong Kong Baptist University · +3
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  2. 2025
    Relation preserving for incremental image retrievalHongsong Wang, A. LiJournal of Electronic Imaging · South China Normal University
  3. 2025
    Towards Differential Optimization: Rehearsal-Free Class-Incremental Learning with Slow Learners and Fast AdaptersYinghong Chen, Huanjia Zhu, Jiali Cai … Bingzhi ChenICASSP · South China Normal University
  4. 2025
    Knowledge-guided prompt-based continual learning: Aligning task-prompts through contrastive hard negativesHengyang Lu, Lauren Lin, Chenyou Fan … Xiao‐Jun WuKnowledge-Based Systems · Jiangnan University · South China Normal University
  5. 2024
    Enhancing Few-Shot Classification without Forgetting Through Multi-level Contrastive ConstraintsBingzhi Chen, Haoming Zhou, Yishu Liu … Guangming LuIEEE International Conference on Multimedia and Expo (ICME) · South China Normal University · Harbin Institute of Technology
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  6. 2024
    Effective Data Selection and Replay for Unsupervised Continual LearningHanmo Liu, Shimin Di, Haoyang Li … Xiaofang ZhouICDE · Hong Kong University of Science and Technology · South China Normal University
  7. 2025
    Lifelong-MonoDepth: Lifelong Learning for Multidomain Monocular Metric Depth EstimationJunjie Hu, Chenyou Fan, Liguang Zhou … Tin Lun LamTNNLS · Chinese University of Hong Kong, Shenzhen · South China Normal University · +2
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  8. 2022
    Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear ConnectorGuoliang Lin, Hanlu Chu, Hanjiang LaiCVPR · Sun Yat-sen University · South China Normal University
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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. 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.