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.

12 papers of 8,653Sort Recent · Most cited
  1. 2026
    TADG: topology-aware and distillation-guided framework for continual knowledge graph embeddingMingsheng Wang, Pengfei Wang, Ming He, Hongbin WangInformation Sciences · Harbin Engineering University · Tokyo Institute of Technology
  2. 2025
    Memory augmented using diffusion model for class-incremental learningQuentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi MurataImage and Vision Computing · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
  3. 2024
    Future-proofing class-incremental learningQuentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi MurataMachine Vision and Applications · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
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  4. 2023
    Class-Incremental Learning using Diffusion Model for Distillation and ReplayQuentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi MurataICCV · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
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  5. 2023
    Generative Replay Inspired by Hippocampal Memory Indexing for Continual Language LearningAru Maekawa, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu OkumuraEACL · Tokyo Institute of Technology · Nara Institute of Science and Technology
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  6. 2021
    Balanced Softmax Cross-Entropy for Incremental LearningQuentin Jodelet, Xin Liu, Tsuyoshi MurataComputer Vision and Image Understanding · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
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  7. 2020
    CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future DirectionsVincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodríguez … Davide MaltoniArtificial Intelligence · University of Bologna · Mila - Quebec Artificial Intelligence Institute · +7
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  8. 2020
    SOINN+, a Self-Organizing Incremental Neural Network for Unsupervised Learning from Noisy Data StreamsChayut Wiwatcharakoses, Daniel BerrarExpert Systems with Applications · Tokyo Institute of Technology
  9. 2019
    Self-Organizing Incremental Neural Networks for Continual LearningChayut Wiwatcharakoses, Daniel BerrarIJCAI · Tokyo Institute of Technology
  10. 2013
    Incremental Learning Framework for Indoor Scene RecognitionAram Kawewong, Rapeeporn Pimup, Osamu HasegawaAAAI · Chiang Mai University · Tokyo Institute of Technology
  11. 2004
    A self-structurizing neural network for online incremental learningOsamu Hasegawa, Furao ShenSociety of Instrument and Control Engineers of Japan · Tokyo Institute of Technology
  12. 1995
    A functional analytic approach to incremental learning in optimally generalizing neural networksSethu Vijayakumar, Haruo OgawaICNN'95 - International Conference on Neural Networks · Tokyo Institute of Technology
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.