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. 2024
    Adaptive Decoupled Prompting for Class Incremental LearningFanhao Zhang, Shiye Wang, Changsheng Li … Guoren WangSpringer LNCS · Beijing Institute of Technology
  2. 2024
    Adapt Without Forgetting: Distill Proximity from Dual Teachers in Vision-Language ModelsMengyu Zheng, Yehui Tang, Zhiwei Hao … Chang XuECCV · The University of Sydney · Huawei Technologies (Canada) · +1
  3. 2024
    FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph LearningJinhui Pang, Changqing Lin, Xiaoshuai Hao … Taisheng HuangACM MM · Beijing Institute of Technology · Samsung (China) · +2
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  4. 2024
    Importance-aware Shared Parameter Subspace Learning for Domain Incremental LearningShiye Wang, Changsheng Li, Jialin Tang … Guoren WangACM International Conference on Multimedia · Beijing Institute of Technology
  5. 2024
    Revisiting class-incremental object detection: An efficient approach via intrinsic characteristics alignment and task decouplingLiang Bai, Hong Song, Tao Feng … Jian YangExpert Systems with Applications · Beijing Institute of Technology · Tsinghua University
  6. 2024
    MoBoo: Memory-Boosted Vision Transformer for Class-Incremental LearningBolin Ni, Xing Nie, Chenghao Zhang … Shiming XiangIEEE TCSVT · Shandong Institute of Automation · Beijing Institute of Technology
  7. 2024
    CTL-I: Infrared Few-Shot Learning via Omnidirectional Compatible Class-IncrementalBiwen Yang, Ruiheng Zhang, Yumeng Liu … Lixin XuLecture notes of the Institute for Computer Sciences, Soc… · Beijing Institute of Technology · Chinese Academy of Sciences · +1
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.