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.

57 papers of 8,653 · showing 51–57Sort Recent · Most cited
  1. 2022
    Dataset Knowledge Transfer for Class-Incremental Learning without MemoryHabib Slim, Eden Belouadah, Adrian Popescu, Darian M. OnchişWACV · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +5
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  2. 2021
    InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive LearningKwot Sin Lee, Ngoc-Trung Tran, Ngai‐Man CheungWACV · University of Cambridge · Snap (United States) · +1
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  3. 2021
    Continual Representation Learning for Biometric IdentificationBo Zhao, Shixiang Tang, Dapeng Chen … Rui ZhaoWACV · Group Sense (China) · University of Edinburgh · +1
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  4. 2021
    Do not Forget to Attend to Uncertainty while Mitigating Catastrophic ForgettingVinod K Kurmi, Badri N. Patro, Venkatesh K. Subramanian, Vinay P. NamboodiriWACV · Indian Institute of Technology Kanpur · University of Bath
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  5. 2021
    Object Recognition with Continual Open Set Domain Adaptation for Home RobotIkki Kishida, Hong Chen, Masaki Baba … Hideki NakayamaWACV · The University of Tokyo
  6. 2020
    Class-incremental Learning via Deep Model ConsolidationJunting Zhang, Jie Zhang, Shalini Ghosh … C.‐C. Jay KuoWACV · University of Southern California · California Southern University · +3
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  7. 2020
    Regularize, Expand and Compress: NonExpansive Continual LearningJie Zhang, Junting Zhang, Shalini Ghosh … Yalin WangWACV · University of Southern California · Arizona State University · +1
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.