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.

8 papers of 8,653Sort Recent · Most cited
  1. 2023
    Slow-Fast Time Parameter Aggregation Network for Class-Incremental Lip ReadingXueyi Zhang, Chengwei Zhang, Tao Wang … Haizhou LiACM International Conference on Multimedia · National University of Defense Technology · Shenzhen Research Institute of Big Data · +3
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  2. 2023
    Continual Learning on Dynamic Graphs via Parameter IsolationPeiyan Zhang, Yuchen Yan, Chaozhuo Li … Sunghun KimSIGIR · Hong Kong University of Science and Technology · Peking University · +3
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  3. 2023
    Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental LearningZeyin Song, Yifan Zhao, Yujun Shi … Yonghong TianCVPR · Peking University · National University of Singapore · +1
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  4. 2023
    Regularizing Second-Order Influences for Continual LearningZhicheng Sun, Yadong Mu, Gang HuaCVPR · Peking University · Peng Cheng Laboratory
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  5. 2023
    Dealing with Cross-Task Class Discrimination in Online Continual LearningYiduo Guo, Bing Liu, Dongyan ZhaoCVPR · Peking University · University of Illinois Chicago
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  6. 2023
    Recent Advances in Class-Incremental LearningDejie Yang, Minghang Zheng, Weishuai Wang … Yang LiuSpringer LNCS · Peking University
  7. 2023
    Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANsZeren Zhang, Xingjian Li, Hong Tao … Chengzhong XuSpringer LNCS · Peking University · Baidu (China) · +2
  8. 2023
    Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action LocalizationTianquan Chen, Bairong Li, Yusheng Tao … Yuesheng ZhuSpringer LNCS · Peking University
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.