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.

17 papers of 8,653Sort Recent · Most cited
  1. 2020
    Simple Lifelong Learning MachinesJoshua T. Vogelstein, Jayanta Dey, Hayden S. Helm … Carey E. PriebeTPAMI · Johns Hopkins University · Baylor College of Medicine · +1
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  2. 2023
    Efficient Self-Supervised Continual Learning with Progressive Task-Correlated Layer FreezingLi Yang, Sen Lin, Fan Zhang … Deliang FanInternational Symposium on Quality Electronic Design (ISQED) · University of North Carolina at Charlotte · University of Houston · +3
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  3. 2024
    Stability through plasticity: Finding robust memories through representational driftMaanasa Natrajan, James E. FitzgeraldbioRxiv · Northwestern University · Howard Hughes Medical Institute · +3
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  4. 2024
    Hyb-Learn: A Framework for On-Device Self-Supervised Continual Learning with Hybrid RRAM/SRAM MemoryFan Zhang, Li Yang, Deliang FanACM/IEEE Design Automation Conference · Johns Hopkins University · University of North Carolina at Charlotte
  5. 2024
    A collective AI via lifelong learning and sharing at the edgeAndrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman … Soheil KolouriNature Machine Intelligence · Loughborough University · Rice University · +21
  6. 2023
    Wakening Past Concepts without Past Data: Class-Incremental Learning from Online PlacebosYaoyao Liu, Yingying Li, Bernt Schiele, Qianru SunWACV · Johns Hopkins University · Max Planck Institute for Informatics · +2
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  7. 2023
    Clustering-based Domain-Incremental LearningChristiaan Lamers, René Vidal, Nabil Belbachir … Paris V. GiampourasICCV · NORCE Research AS · University of Pennsylvania · +1
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  8. 2023
    Online Hyperparameter Optimization for Class-Incremental LearningYaoyao Liu, Yingying Li, Bernt Schiele, Qianru SunAAAI · Johns Hopkins University · Max Planck Institute for Informatics · +2
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  9. 2022
    Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR ModelsYuki Takashima, Shota Horiguchi, Shinji Watanabe … Yohei KawaguchiInterspeech · Hitachi (Japan) · Johns Hopkins University · +1
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  10. 2022
    Lifelong learning for robust AI systemsGautam K. Vallabha, J. MarkowitzArtificial Intelligence and Machine Learning for Multi-Do… · Johns Hopkins University
  11. 2021
    Rethinking Architecture Design for Tackling Data Heterogeneity in Federated LearningLiangqiong Qu, Yuyin Zhou, Paul Pu Liang … Daniel L. RubinCVPR · Stanford University · University of California, Santa Cruz · +2
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  12. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
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  13. 2021
    Coarse-To-Fine Incremental Few-Shot LearningXiang Xiang, Yuwen Tan, Qian Wan … Gregory D. HagerSpringer LNCS · Huazhong University of Science and Technology · Johns Hopkins University
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  14. 2021
    Omnidirectional Transfer for Quasilinear Lifelong LearningJayanta Dey, Joshua T Vogelstein, Hayden S. Helm … Carey E. PriebeResearch Square · Johns Hopkins University · Baylor College of Medicine · +1
  15. 2020
    Incremental Meta-Learning via Indirect Discriminant AlignmentQing Liu, Orchid Majumder, Alessandro Achille … Stefano SoattoECCV · Johns Hopkins University · Amazon (Germany)
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  16. 2020
    Incremental Few-Shot Meta-learning via Indirect Discriminant AlignmentQing Liu, Orchid Majumder, Alessandro Achille … Stefano SoattoSpringer LNCS · Johns Hopkins University · Amazon (United States)
  17. 2019
    Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine TranslationBrian J. Thompson, Jeremy Gwinnup, Huda Khayrallah … Philipp KoehnNAACL · Johns Hopkins University
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.