Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

9 papers of 11,817Sort Recent · Most cited
  1. 2022
    Incremental Training of Face Morphing DetectorsGuido Borghi, Gabriele Graffieti, Annalisa Franco, Davide MaltoniICPR · University of Bologna
  2. 2022
    Effects of Auxiliary Knowledge on Continual LearningGiovanni Bellitto, Matteo Pennisi, Simone Palazzo … Simone CalderaraICPR · University of Catania · University of Modena and Reggio Emilia
    PDF ↗
  3. 2022
    Rethinking Task-Incremental Learning BaselinesMd. Sazzad Hossain, Pritom Saha, Townim Faisal Chowdhury … Nabeel MohammedICPR · Grameenphone (Bangladesh) · North South University
    PDF ↗
  4. 2022
    Class Incremental Learning based on Local Structure Constraints in Feature SpaceYinan Li, Ronghua Luo, Zhen-Ming HuangICPR · South China University of Technology
  5. 2022
  6. 2022
    Continual Learning via Dynamic ProgrammingRanganath Krishnan, Prasanna BalaprakashICPR · Argonne National Laboratory
  7. 2022
    Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot LearningJansen Keith L. Domoguen, Prospero C. NavalICPR · University of the Philippines Diliman
  8. 2022
    KRNet: Towards Efficient Knowledge ReplayYingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang PuICPR
    PDF ↗
  9. 2022
    Feature Distribution Distillation-Based Few Shot Class Incremental LearningJuntao Zhu, Guangle Yao, Wenlong Zhou … Wei ZhangICPR · Chengdu University of Technology · Yibin University · +4
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.