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. 2026
    FAT-TAG: Mitigating Forgetting in Task-Free Temporal Graph Class Incremental LearningJiyuan Feng, Zhao Liu, Dongyi Zheng … Qing LiaoKDD · Harbin Institute of Technology · Peng Cheng Laboratory · +1
  2. 2026
    Sample-Aware Knowledge Association and Enhancement for Open-Vocabulary Continual LearningZhilin Zhu, Zhiheng Ma, Yabin Wang … Xiaopeng HongIJCV · Harbin Institute of Technology · Peng Cheng Laboratory · +2
  3. 2026
    Sparse Orthogonal Parameters Tuning for Continual LearningKun-Peng Ning, Hai-Jian Ke, Yuyang Liu … Yuan LiIJCV · Peking University Shenzhen Hospital · Peng Cheng Laboratory
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  4. 2026
    Generalized few-shot intent detection by prompt learning without forgettingChaiyut Luoyiching, Yangning Li, Rongsheng Li … Hong‐Gee KimNeural Computing and Applications · University Town of Shenzhen · Tsinghua University · +3
  5. 2026
    Class-Incremental Cloud-Device Collaborative Adaptation With Contrastive Learning in Dynamic Changing EnvironmentsYushi Zeng, Haopeng Ren, Yi Cai … Qing LiTNNLS · South China University of Technology · Guangzhou University · +3
  6. 2026
    Functionality Separation: Rethinking Dual-Stream Networks for Class-Incremental LearningQi Gao, Xiaoyan Li, Zhongfan Sun … Wen GaoIEEE TCSVT · Beijing University of Technology · Imperial College London · +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.