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.

15 papers of 11,817Sort Recent · Most cited
  1. 2026
    Federated continual learning with joint diffusion-based generative replayYouhuizi Li, Yu Chen, Yiran Ma … Shuyuan HuApplied Soft Computing
  2. 2026
    SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential RecommendationXiaomeng Song, Xinru Wang, Han-Bing Wang … Zhumin ChenarXiv
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  3. 2026PDF ↗
  4. 2025
    CSTIN-DM: A Generalizable and Adaptive Framework for Vehicle Trajectory PredictionTingyi Zhao, Yu Chen, Ying Zhang … Wencheng YuanCAA International Conference on Vehicular Control and Int…
  5. 2025
    Online Continual Learning via Spiking Neural Networks with Sleep Enhanced Latent ReplayErliang Lin, W. Luo, Wei Jia … Shaofu YangInternational Conference on Intelligent Control and Infor…
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  6. 2024
    BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object DetectionYu Chen, Liyan Ma, Liping Jing, Jian-hong YuPattern Recognition
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  7. 2020
    CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future DirectionsVincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodríguez … Davide MaltoniArtificial Intelligence · University of Bologna · Mila - Quebec Artificial Intelligence Institute · +7
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  8. 2021
    Continual Density Ratio Estimation in an Online SettingYu Chen, Song Liu, Tom Diethe, Peter FlacharXiv
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  9. 2021
    Self-incremental learning vector quantization with human cognitive biasesNobuhito Manome, Shuji Shinohara, Tatsuji Takahashi … Ung‐il ChungScientific Reports · Tokyo University of Information Sciences · The University of Tokyo · +1
  10. 2020
  11. 2020
  12. 2020PDF ↗
  13. 2020
  14. 2019
    Incremental Object Learning From Contiguous ViewsStefan Stojanov, Samarth Mishra, Ngoc Anh Thai … James M. RehgCVPR · Georgia Institute of Technology · Indiana University Bloomington
  15. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
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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.