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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2025PDF ↗
  3. 2024
    Potential Knowledge Extraction Network for Class-Incremental LearningXidong Xi, Guitao Cao, Wenming Cao … He RenNeurocomputing · Shanghai Key Laboratory of Trustworthy Computing · East China Normal University · +1
  4. 2024
    Class Balance Matters to Active Class-Incremental LearningZhenhong Huang, Ze Chen, Yuanze Li … Wangmeng ZuoACM MM · Harbin Institute of Technology · Megvii (China) · +2
    PDF ↗
  5. 2024
    Learning Task-Specific Initialization for Effective Federated Continual Fine-Tuning of Foundation Model AdaptersDanni Peng, Yuan Wang, Huazhu Fu … Rick Siow Mong GohIEEE Conference on Artificial Intelligence (CAI) · Agency for Science, Technology and Research · Institute of High Performance Computing
  6. 2024PDF ↗
  7. 2024
    Research on Incremental Learning Methods Based on Sample and Category Prototype PlaybackJiamin Zhi, Yong LiuInternational Journal of Computer Science and Information… · Henan University of Science and Technology
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  8. 2024PDF ↗
  9. 2023
    Cross-Modal Alternating Learning With Task-Aware Representations for Continual LearningWujin Li, Bin-Bin Gao, Bizhong Xia … Feng ZhengIEEE Trans. Multimedia · University Town of Shenzhen · Tsinghua University · +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.