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.

9 papers of 8,653Sort Recent · Most cited
  1. 2023
    Ada-QPacknet - Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive PruningMarcin Pietroń, Dominik Żurek, Kamil Faber, Roberto CorizzoFrontiers · AGH University of Krakow · American University
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  2. 2023
    Offline Experience Replay for Continual Offline Reinforcement LearningSibo Gai, Donglin Wang, Li HeFrontiers · Fudan University · Westlake University
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  3. 2023
    Data-Free Class-Incremental Learning with Implicit Representation of PrototypesTianwen Yang, Leixiong Huang, Ronghua LuoFrontiers · South China University of Technology
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  4. 2023
    Adaptive Self-Supervised Continual LearningLilei Wu, Zhen Wang, Jie LiuFrontiers · National University of Defense Technology · Tsinghua University
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  5. 2023
    R-STAR: Robust Self-Taught Task-Wise Reweighting for Rehearsal-Based Class Incremental LearningYutian Luo, Yizhao Gao, Haoran Wu … Zhiwu LuFrontiers · Renmin University of China · China United Network Communications Group (China)
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  6. 2023
    Few-Shot Continual Learning Based on Vector Symbolic ArchitecturesGeethan Karunaratne, Michael Hersche, Giovanni Cherubini … Abbas RahimiFrontiers · IBM Research - Zurich
  7. 2023
    Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learningMuhammad Burhan Hafez, Tilman Immisch, Tom Weber, Stefan WermterFrontiers · Universität Hamburg · Hamburg University of Technology
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  8. 2023
    A survey and perspective on neuromorphic continual learning systemsRicha Mishra, Manan SuriFrontiers · Indian Institute of Technology Delhi
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  9. 2023
    On-device synaptic memory consolidation using Fowler-Nordheim quantum-tunnelingMustafizur Rahman, Subhankar Bose, Shantanu ChakrabarttyFrontiers · Washington University in St. Louis
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.