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. 2025
    One-for-More: Continual Diffusion Model for Anomaly DetectionXiaofan Li, Xin Tan, Zhuo Chen … Yuan XieCVPR · East China Normal University · Xiamen University · +2
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  2. 2025
    Few-Shot Class-Incremental Learning via Asymmetric Supervised Contrastive LearningDuo Liu, Linglan Zhao, Zhongqiang Zhang … Liang WangIEEE TCSVT · Shanghai Jiao Tong University · Tencent (China) · +4
  3. 2025
    Uncertainty-Calibrated Test-Time Model Adaptation Without ForgettingMingkui Tan, Guohao Chen, Jiaxiang Wu … Shuaicheng NiuTPAMI · South China University of Technology · National University of Singapore · +2
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  4. 2025
    Instruct Where the Model Fails: Generative Data Augmentation via Guided Self-contrastive Fine-tuningWeijian Ma, Ruoxin Chen, Ke-Yue Zhang … Shouhong DingAAAI · Fudan University · Tencent (China)
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  5. 2025
    An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-TuningYun Luo, Zhen Yang, Fandong Meng … Yue ZhangIEEE Transactions · Westlake University · Tencent (China)
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  6. 2025
    Source-Free Elastic Model Adaptation for Vision-and-Language NavigationMingkui Tan, Peihao Chen, Hongyan Zhi … Runhao ZengIEEE Trans. Multimedia · South China University of Technology · Tencent (China) · +4
  7. 2025
    Exploring Stability-Plasticity Trade-Offs for Continual Named Entity RecognitionDuzhen Zhang, Chenxing Li, Jiahua Dong … Dong YuIEEE Transactions · Mohamed bin Zayed University of Artificial Intelligence · Tencent (China) · +3
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  8. 2025
    WI3D: Weakly Incremental 3D Detection via Vision Foundation ModelsMingsheng Li, Sijin Chen, Shengji Tang … Tao ChenIEEE Trans. Multimedia · Fudan University · Agency for Science, Technology and Research · +2
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.