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.

10 papers of 8,653Sort Recent · Most cited
  1. 2025
    Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental LearningJizhou Han, Chenhao Ding, Yuhang He … Yihong GongIEEE TCSVT · Xi'an Jiaotong University · Shenzhen University · +2
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  2. 2025
    Max-Informative Unlabeled Sample Replay for Semi-Supervised Class-Incremental Learning in Audio ClassificationQiang Wang, Ao Shen, Dawei Feng … Huaimin WangIEEE International Conference on Joint Cloud Computing (JCC) · National University of Defense Technology
  3. 2025
    Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You NeedQiang Wang, Xiang Song, Yuhang He … Yihong GongCVPR · Xi'an Jiaotong University
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  4. 2025
    Dynamic Integration of Task-Specific Adapters for Class Incremental LearningJiashuo Li, Shaokun Wang, Bo Qian … Yihong GongCVPR · Xi'an Jiaotong University
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  5. 2025
    Learning Endogenous Attention for Incremental Object DetectionXiang Song, Yuhang He, Jingyuan Li … Yihong GongCVPR · Xi'an Jiaotong University
  6. 2025PDF ↗
  7. 2025
    Memory replay with unlabeled data for semi-supervised class-incremental learning via temporal consistencyQiang Wang, Kele Xu, Dawei Feng … Huaimin WangFrontiers of Computer Science · National University of Defense Technology
  8. 2025
    Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identificationShiben Liu, Huijie Fan, Qiang Wang … Liang-Qiong QuIEEE Trans. Multimedia
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  9. 2025PDF ↗
  10. 2025
    Imbalance Mitigation for Continual Learning via Knowledge Decoupling and Dual Enhanced Contrastive LearningZhong Ji, Zhanyu Jiao, Qiang Wang … Jungong HanTNNLS · Tianjin University · Beijing Academy of Artificial Intelligence · +2
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.