Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 8,653Sort Recent · Most cited
  1. 2022
    MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance FieldsJaeyoung Chung, K. Lee, Sungyong Baik, Kyoung Mu LeeIEEE Access · Seoul National University · Hanyang University
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  2. 2022
    Rebalancing Batch Normalization for Exemplar-Based Class-Incremental LearningSungmin Cha, Sungjun Cho, Dasol Hwang … Taesup MoonCVPR · Seoul National University · University of Illinois Chicago
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  3. 2022
    Brain-inspired Predictive Coding Improves the Performance of Machine Challenging TasksJangho Lee, Jeonghee Jo, Byoung-Hwa Lee … Sungroh YoonFrontiers · Seoul National University · Electronics and Telecommunications Research Institute · +1
  4. 2022
    Class-Incremental Learning by Knowledge Distillation with Adaptive Feature ConsolidationMinsoo Kang, Jaeyoo Park, Bohyung HanCVPR · Seoul National University
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  5. 2022
    Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesJihwan Bang, Hyunseo Koh, Seulki Park … Jonghyun ChoiCVPR · NAVER Cloud (South Korea) · Naver (South Korea) · +2
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  6. 2022
    Visual Tracking by Adaptive Continual Meta-LearningJanghoon Choi, Sungyong Baik, Myungsub Choi … Kyoung Mu LeeIEEE Access · Kookmin University · Seoul National University · +1
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  7. 2022
    Incremental Learning With Adaptive Model Search and a Nominal Loss ModelChanho Ahn, Eunwoo Kim, Songhwai OhIEEE Access · Seoul National University · Chung-Ang University
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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.