Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

9 papers of 6,984Sort Recent · Most cited
  1. 2024
    Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and MitigationBaptiste Wagner, Denis Pellerin, Sylvain HuetIEEE Access · Institut polytechnique de Grenoble · Centre National de la Recherche Scientifique · +1
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  2. 2024
    Brain-Inspired Continual Learning: Robust Feature Distillation and Re-Consolidation for Class Incremental LearningHikmat Khan, Nidhal Bouaynaya, Ghulam RasoolIEEE Access · Rowan University · Moffitt Cancer Center
  3. 2024
    Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryNaoki Masuyama, Yusuke Nojima, Yuichiro Toda … Naoyuki KubotaIEEE Access · Osaka Metropolitan University · Okayama University · +3
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  4. 2024PDF ↗
  5. 2024
    MixER: Mixup-Based Experience Replay for Online Class-Incremental LearningWon-Seon Lim, Yu Zhou, Dae‐Won Kim, Jaesung LeeIEEE Access · Chung-Ang University · Shenzhen University
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  6. 2024
    Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and ApplicationsMohammed Awal Kassim, Herna L. Viktor, Wojtek MichalowskiIEEE Access · University of Ottawa
  7. 2024
    Class Incremental Learning With Large Domain ShiftKamin Lee, Hyoeun Kim, Geunjae Choi … Nojun KwakIEEE Access · Seoul National University of Science and Technology · LG (South Korea)
  8. 2024
    Memory-Efficient Continual Learning Object Segmentation for Long VideosAmir Nazemi, Mohammad Javad Shafiee, Zahra Gharaee, Paul FieguthIEEE Access · University of Waterloo
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  9. 2024
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.