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.

7 papers of 11,817Sort Recent · Most cited
  1. 2022
    BeGin: Extensive Benchmark Scenarios and an Easy-to-use Framework for Graph Continual LearningJihoon Ko, Shinhwan Kang, Taehyung Kwon … Kijung ShinACM Transactions · Korea Advanced Institute of Science and Technology · International Graduate School of English
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  2. 2022
    Few-shot Incremental Event DetectionHao Wang, Hanwen Shi, Jianyong DuanACM Transactions · North China University of Technology
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  3. 2022
    Continual Recognition with Adaptive Memory UpdateXuanrong Yao, Xin Wang, Yue Liu, Wenwu ZhuACM Transactions · Tsinghua University
  4. 2022
    CL2R: Compatible Lifelong Learning RepresentationsNiccolò Biondi, Federico Pernici, Matteo Bruni … Alberto Del BimboACM Transactions · University of Florence
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  5. 2022
    Graph Deep Factors for Probabilistic Time-series ForecastingHongjie Chen, Ryan A. Rossi, Kanak Mahadik … Hoda EldardiryACM Transactions · Virginia Tech · Adobe Systems (United States)
  6. 2022
    Testing the Plasticity of Reinforcement Learning-based SystemsMatteo Biagiola, Paolo TonellaACM Transactions · Università della Svizzera italiana
  7. 2022
    RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object DetectionDongbao Yang, Yu Zhou, Wei Shi … Weiping WangACM Transactions · University of Chinese Academy of Sciences · Chinese Academy of Sciences · +1
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.