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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.

13 papers of 8,653Sort Recent · Most cited
2009 All papers Clear all
  1. 2009
    Avoiding Catastrophic Forgetting by a Dual-Network Memory Model Using a Chaotic Neural NetworkM. HattoriPreprint
  2. 2009
    The dark side of incremental learning: A model of cumulative semantic interference during lexical access in speech productionGary M. Oppenheim, Gary S. Dell, Myrna F. SchwartzCognition · University of Illinois Urbana-Champaign · Urbana University · +1
  3. 2009
    Self-organization of asymmetric associative networksChristian Albers, Klaus PawelzikBMC Neuroscience · University of Bremen
  4. 2009
    An incremental learning algorithm for supervised neural network with contour preserving classificationPiyabute Fuangkhon, Thitipong TanprasertInternational Conference on Electrical Engineering/Electr… · Assumption University
  5. 2009
    Evolved Dual Weight Neural Architectures to Facilitate Incremental LearningJohn A. BullinariaInternational Joint Conference on Computational Intelligence · University of Birmingham
  6. 2009
    Online Incremental Face Recognition System Using Eigenface Feature and Neural ClassifierSeiichi Ozawa, Shigeo Abe, Shaoning Pang, Nikola KasabovState of the Art in Face Recognition
  7. 2009
    Capacity of Memory and Error Correction Capability in Chaotic Neural Networks with Incremental LearningToshinori Deguchi, Keisuke Matsuno, Toshiki Kimura, Naohiro IshiiStudies in computational intelligence · National Institute of Technology, Gifu College · Aichi Institute of Technology
  8. 2009
    Weights Updated Voting for Ensemble of Neural Networks Based Incremental LearningJianjun Liu, Shengping Xia, Weidong Hu, Wenxian YuSpringer LNCS · National University of Defense Technology
  9. 2009
    Structural plasticity and memory: Catastrophic forgetting, amnesia, and the spacing effectEdgar KörnerFrontiers
  10. 2009
    1P-207 Incremental learning by lattice neural networks(Neuronal Circuit & Information processing, The 47th Annual Meeting of the Biophysical Society of Japan)Daisuke Uragami, Hiroyuki OhtaSeibutsu Butsuri · Gakushuin University · National Defense Medical College
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  11. 2009
    A Proposal For Continual Learning In RoboticsU. Nehmzow, L. RianoPreprint
  12. 2009
    A Survey of Research Work on Incremental Learning Based on Feedforward Neural NetworksWen-Xian YuPreprint
  13. 2009
    Meaningful Representations Prevent Catastrophic InterferenceJ. Bieger, I. Sprinkhuizen-Kuyper, I. V. van RooijPreprint
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.