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.

8 papers of 6,984Sort Recent · Most cited
  1. 2024
    Fast Online Adaptation of Visual SLAM via Variational Information Transfer and PreservationSangni Xu, Hao Xiong, Qiuxia Wu … Zhiyong WangACM International Conference on Multimedia in Asia · South China University of Technology · Macquarie University · +2
  2. 2024
    Adapt Without Forgetting: Distill Proximity from Dual Teachers in Vision-Language ModelsMengyu Zheng, Yehui Tang, Zhiwei Hao … Chang XuECCV · The University of Sydney · Huawei Technologies (Canada) · +1
  3. 2024
    Continual Learning From a Stream of APIsEnneng Yang, Zhenyi Wang, Li Shen … Dacheng TaoTPAMI · Northeastern University · University of Maryland, College Park · +4
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  4. 2024
    Topology-aware Embedding Memory for Continual Learning on Expanding NetworksXikun Zhang, Dongjin Song, Yixin Chen, Dacheng TaoKDD · The University of Sydney · University of Connecticut · +1
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  5. 2024
    Continual Learning for Temporal-Sensitive Question AnsweringWanqi Yang, Yunqiu Xu, Yanda Li … Ling ChenIJCNN · University of Technology Sydney · The University of Sydney · +1
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  6. 2024
    Ricci Curvature-Based Graph Sparsification for Continual Graph Representation LearningXikun Zhang, Dongjin Song, Dacheng TaoTNNLS · The University of Sydney · University of Connecticut
  7. 2024
    Overcoming Catastrophic Forgetting in Continual Learning by Exploring Eigenvalues of Hessian MatrixYajing Kong, Liu Liu, Huanhuan Chen … Dacheng TaoTNNLS · The University of Sydney · University of Science and Technology of China · +2
  8. 2024
    Incremental Embedding Learning With Disentangled Representation TranslationKun Wei, Da Chen, Yuhong Li … Dacheng TaoTNNLS · Xidian University · Alibaba Group (China) · +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. 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.