Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 6,984Sort Recent · Most cited
  1. 2023
    Incremental Graph Classification by Class Prototype Construction and AugmentationYixin Ren, Li Ke, Dong Li … Shuigeng ZhouCIKM · Fudan University · Alibaba Group (China)
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  2. 2023
    Online Prototype Learning for Online Continual LearningYujie Wei, Jiaxin Ye, Zhizhong Huang … Hongming ShanICCV · Fudan University · Shanghai Center for Brain Science and Brain-Inspired Technology · +1
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  3. 2023
    MRN: Multiplexed Routing Network for Incremental Multilingual Text RecognitionTianlun Zheng, Zhineng Chen, Bingchen Huang … Yu–Gang JiangICCV · Fudan University
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  4. 2023
    Offline Experience Replay for Continual Offline Reinforcement LearningSibo Gai, Donglin Wang, Li HeFrontiers · Fudan University · Westlake University
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  5. 2023
    Class-Incremental Generalized Zero-Shot LearningZhenfeng Sun, Rui Feng, Yanwei FuMultimedia Tools and Applications · Wuhu Hit Robot Technology Research Institute · Fudan University
  6. 2023PDF ↗
  7. 2023
    Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NERRuotian Ma, Xuanting Chen, Lin Zhang … Yun Wen ChenACL · Fudan University · Wanfang Data (China)
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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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.