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.

68 papers of 8,653 · showing 51–68Sort Recent · Most cited
  1. 2021
    Condensed Composite Memory Continual LearningFelix Wiewel, Bin YangIJCNN · University of Stuttgart
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  2. 2021
    Bilevel Continual LearningAmmar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe YinIJCNN · Heidelberg University
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  3. 2021
    Evolutionary NAS in Light of Model Stability for Accurate Continual LearningXiaocong Du, Zheng Li, Jingbo Sun … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
  4. 2020
    Catastrophic forgetting and mode collapse in GANsHoang Thanh-Tung, Truyen TranIJCNN · Deakin University · Allen Institute for Artificial Intelligence
  5. 2020
    Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading ComprehensionYing Xu, Xu Zhong, Antonio Jimeno Yepes, Jey Han LauIJCNN · IBM Research - Australia · The University of Melbourne
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  6. 2020
    OvA-INN: Continual Learning with Invertible Neural NetworksGuillaume Hocquet, Olivier Bichler, Damien QuerliozIJCNN · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +2
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  7. 2020
    Online Knowledge Acquisition with the Selective Inherited ModelXiaocong Du, Shreyas Kolala Venkataramanaiah, Zheng Li … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
  8. 2019
    A New Knowledge Distillation for Incremental Object DetectionLi Chen, Chunyan Yu, Lvcai ChenIJCNN · Fuzhou University
  9. 2019
    Effect of Pruning on Catastrophic Forgetting in Growing Dual Memory NetworksWei Shiung Liew, Chu Kiong Loo, Vadym Gryshchuk … Stefan WermterIJCNN · University of Malaya · Universität Hamburg
  10. 2019
    Stable Network MorphismTao Wei, Changhu Wang, Chang Wen ChenIJCNN · University at Buffalo, State University of New York · Chinese University of Hong Kong, Shenzhen
  11. 2019
    Spatial Map Learning with Self-Organizing Adaptive Recurrent Incremental NetworkWei Hong Chin, Naoyuki Kubota, Chu Kiong Loo … Honghai LiuIJCNN · Tokyo Metropolitan University · University of Malaya · +1
  12. 2019
    Selective Hypothesis Transfer for Lifelong LearningDiana Benavides‐Prado, Yun Sing Koh, Patricia RiddleIJCNN · University of Auckland
  13. 2015
    Reduction of catastrophic forgetting with transfer learning and ternary output codesSteven Gutstein, Ethan StumpIJCNN · DEVCOM Army Research Laboratory
  14. 2010
    An incremental learning method for neural networks in adaptive environmentsBeatriz Pérez‐Sánchez, Óscar Fontenla-Romero, Bertha Guijarro‐BerdiñasIJCNN · Universidade da Coruña
  15. 2008
    A comparison of fuzzy ARTMAP and Gaussian ARTMAP neural networks for incremental learningÉric Granger, Jean-François Connolly, Robert SabourinIJCNN · École de Technologie Supérieure
  16. 1999
    Pattern classification by an incremental learning fuzzy neural networkGary G. Yen, Phayung MeesadIJCNN · Oklahoma State University
  17. 1991
    Incremental learning with rule-based neural networksCharles M. Higgins, R.M. GoodmanIJCNN · California Institute of Technology
  18. 2002
    Reducing computations in incremental learning for feedforward neural network with long-term memoryM. Kobyashi, Abu Sarwar Zamani, Seiichi Ozawa, Shigeo AbeIJCNN · Kobe 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. 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.