Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

117 papers of 4,574 · showing 101–117Sort Recent · Most cited
  1. 2021
    Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingAnna Kukleva, Hilde Kuehne, Bernt SchieleICCV · Max Planck Society · Max Planck Innovation · +4
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  2. 2021
    Wanderlust: Online Continual Object Detection in the Real WorldJianren Wang, Xin Wang, Yue Shang-Guan, Abhinav GuptaICCV · Carnegie Mellon University · Microsoft Research (United Kingdom) · +1
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  3. 2021
    Class-Incremental Learning for Action Recognition in VideosJaeyoo Park, Minsoo Kang, Bohyung HanICCV · Seoul National University
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  4. 2021
    Continual Learning on Noisy Data Streams via Self-Purified ReplayChris Dongjoo Kim, Jinseo Jeong, Sangwoo Moon, Gunhee KimICCV · Seoul National University
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  5. 2021
    Online Continual Learning For Visual Food ClassificationJiangpeng He, Fengqing ZhuICCV · Purdue University West Lafayette
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  6. 2021
    Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential ObservationsZike Yan, Yuxin Tian, Xuesong Shi … Hongbin ZhaICCV · King University · Peking University · +1
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  7. 2021
    Few-Shot and Continual Learning with Attentive Independent MechanismsEugene Lee, Cheng‐Han Huang, Chen‐Yi LeeICCV · National Yang Ming Chiao Tung University
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  8. 2021
    Continual Learning for Image-Based Camera LocalizationShuzhe Wang, Zakaria Laskar, Iaroslav Melekhov … Juho KannalaICCV · Aalto University · Kaneka (United States)
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  9. 2021PDF ↗
  10. 2021PDF ↗
  11. 2019
    Incremental Learning Techniques for Semantic SegmentationUmberto Michieli, Pietro ZanuttighICCV · University of Padua
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  12. 2019
    Overcoming Catastrophic Forgetting With Unlabeled Data in the WildKibok Lee, Kimin Lee, Jinwoo Shin, Honglak LeeICCV · Korea Advanced Institute of Science and Technology · University of Michigan · +1
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  13. 2019
    Lifelong GAN: Continual Learning for Conditional Image GenerationMengyao Zhai, Lei Chen, Fred Tung … Greg MoriICCV · Simon Fraser University · Borealis (Austria)
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  14. 2019
    ACE: Adapting to Changing Environments for Semantic SegmentationZuxuan Wu, Xin Wang, Joseph E. Gonzalez … Larry S. DavisICCV · Berkeley College · University of California, Berkeley · +1
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  15. 2019
    Continual Learning by Asymmetric Loss Approximation With Single-Side OverestimationDong-Min Park, Seokil Hong, Bohyung Han, Kyoung Mu LeeICCV · Seoul National University · Samsung (South Korea)
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  16. 2017
    Incremental Learning of Object Detectors without Catastrophic ForgettingKonstantin Shmelkov, Cordelia Schmid, Karteek AlahariICCV · Institut polytechnique de Grenoble · Centre National de la Recherche Scientifique · +3
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  17. 2017
    Encoder Based Lifelong LearningAmal Rannen, Rahaf Aljundi, Matthew B. Blaschko, Tinne TuytelaarsICCV · IMEC · KU Leuven
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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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.