Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

14 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2025
    CoRA: Covariate-Aware Adaptation of Time Series Foundation ModelsGuo Qin, Zhi Chen, Yong Liu … Mingsheng LongarXiv
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  3. 2025PDF ↗
  4. 2025
    Research on Edge oriented Federated Learning Training OptimizationWenzhe Zhang, Yong Liu, Xiaoli SongJournal of Computing and Electronic Information Management
  5. 2025PDF ↗
  6. 2025PDF ↗
  7. 2024
    Potential Knowledge Extraction Network for Class-Incremental LearningXidong Xi, Guitao Cao, Wenming Cao … He RenNeurocomputing
  8. 2024
    Class Balance Matters to Active Class-Incremental LearningZitong Huang, Ze Chen, Yuanze Li … Wangmeng ZuoACM MM
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  9. 2024
    Learning Task-Specific Initialization for Effective Federated Continual Fine-Tuning of Foundation Model AdaptersDanni Peng, Yuan Wang, Huazhu Fu … R. GohConference on Algebraic Informatics
  10. 2024PDF ↗
  11. 2024
    Research on Incremental Learning Methods Based on Sample and Category Prototype PlaybackJiamin Zhi, Yong LiuInternational Journal of Computer Science & Information T…
  12. 2024PDF ↗
  13. 2024
    Cross-Modal Alternating Learning With Task-Aware Representations for Continual LearningWu-Jun Li, Bin-Bin Gao, Bizhong Xia … Feng ZhengIEEE Trans. Multimedia
  14. 2005
    Incremental Learning with the Neural Network TreesT. Takeda, Qiangfu Zhao, Yong LiuNeural Parallel Sci. Comput.
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. 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.