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.

6 papers of 8,653Sort Recent · Most cited
  1. 2022
    A robust and anti-forgettiable model for class-incremental learningJianting Chen, Yang XiangApplied Intelligence · Tongji University
  2. 2022
    Reminding the incremental language model via data-free self-distillationHan Wang, Ruiliu Fu, Chengzhang Li … Qingwei ZhaoApplied Intelligence · Chinese Academy of Sciences · Institute of Acoustics · +1
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  3. 2022
    RT-Net: replay-and-transfer network for class incremental object detectionBo Cui, Guyue Hu, Shan YuApplied Intelligence · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +4
  4. 2022
    Continual learning via region-aware memoryKai Zhao, Zhenyong Fu, Jian YangApplied Intelligence · Nanjing University of Science and Technology
  5. 2022
    Learning a dual-branch classifier for class incremental learningLei Guo, Gang Xie, Youyang Qu … Lei CuiApplied Intelligence · Taiyuan University of Technology · Taiyuan University of Science and Technology · +1
  6. 2022
    Self-updating continual learning classification method based on artificial immune systemXin Sun, Haotian Wang, Shulin Liu … Haihua XiaoApplied Intelligence · Shanghai University · Changzhou University
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.