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.

8 papers of 11,817Sort Recent · Most cited
  1. 2022
    UNIKD: UNcertainty-Filtered Incremental Knowledge Distillation for Neural Implicit RepresentationMengqi Guo, Li Chen, Hanlin Chen, Gim Hee LeeSpringer LNCS · National University of Singapore
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  2. 2022
    Learning to Predict Gradients for Semi-Supervised Continual LearningYan Luo, Yongkang Wong, Mohan Kankanhalli, Qi ZhaoTNNLS · University of Minnesota · Harvard University · +2
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  3. 2022
    Label-Efficient Online Continual Object Detection in Streaming VideoJay Zhangjie Wu, David Junhao Zhang, Wynne Hsu … Mike Zheng ShouICCV · National University of Singapore · Agency for Science, Technology and Research · +1
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  4. 2022
    Learning to Learn: How to Continuously Teach Humans and MachinesParantak Singh, You Li, Ankur Sikarwar … Mengmi ZhangICCV · Agency for Science, Technology and Research · Nanyang Technological University · +5
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  5. 2022
    Contrastive R-CNN for Incremental Learning in Object DetectionPeisheng Qian, Kai Zheng, Cen Chen … Hui Li TanIEEE Smartworld, Ubiquitous Intelligence & Computing,… · Agency for Science, Technology and Research · Institute for Infocomm Research · +2
  6. 2022
    Efficient Learning of Interpretable Classification RulesBishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. MeelJournal of Artificial Intelligence Research · National University of Singapore
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  7. 2022
    RT-Net: replay-and-transfer network for class incremental object detectionBo Cui, Guyue Hu, Shan YuApplied Intelligence · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +4
  8. 2022
    Controlling Soft Robotic Arms Using Continual LearningFrancesco Piqué, Hari Teja Kalidindi, Lorenzo Fruzzetti … Egidio FaloticoRA-L · Scuola Superiore Sant'Anna · National University of Singapore
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.