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.

13 papers of 8,653Sort Recent · Most cited
  1. 2026
    Self-adaptive Low-Rank Adaptation for Class-Incremental LearningYiming Song, Qiqi Duan, Lijun Sun … Yuhui ShiSpringer LNCS · Southern University of Science and Technology · Jiangxi University of Finance and Economics · +3
  2. 2023
    A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual LearningNaoki Masuyama, Takanori Takebayashi, Yusuke Nojima … Stefan WermterNeural Computing and Applications · Osaka Prefecture University · University of Malaya · +2
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  3. 2026
    VFA: Empowering Multilingual MLLMs via Vision-Free AdaptationYixia Li, Yaqing Shi, Zhiwen Ruan … Furu WeiACL · Southern University of Science and Technology · Shanghai University of Finance and Economics · +3
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  4. 2025
    Analytic Continual Test-Time Adaptation for Multi-Modality CorruptionYufei Zhang, Yicheng Xu, Hongxin Wei … Huiping ZhuangACM International Conference on Multimedia · South China University of Technology · Institute of Science Tokyo · +2
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  5. 2025
    Optimized Model Merging Initialization for Multimodal Continual LearningJinglong Yang, Liang Huang, Jianguo ZhangInternational Workshop on Multi-Sensorial Media and Appli… · City University of Hong Kong · Southern University of Science and Technology
  6. 2025
    PLAN: Proactive Low-Rank Allocation for Continual LearningX Wang, Zhan Zhuang, Y ZhangICCV · Southern University of Science and Technology
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  7. 2025
    ProtoGuard-Guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud SegmentationHaosheng Li, Yuecong Xu, Junjie Chen, Kemi DingRA-L · Southern University of Science and Technology · National University of Singapore
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  8. 2024
    Learning a Low-Rank Feature Representation: Achieving Better Trade-Off Between Stability and Plasticity in Continual LearningZhenrong Liu, Yang Li, Yi Gong, Yik‐Chung WuICASSP · Southern University of Science and Technology · University of Hong Kong · +1
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  9. 2024
    Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryNaoki Masuyama, Yusuke Nojima, Yuichiro Toda … Naoyuki KubotaIEEE Access · Osaka Metropolitan University · Okayama University · +3
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  10. 2023
    Cross-Modal Alternating Learning With Task-Aware Representations for Continual LearningWujin Li, Bin-Bin Gao, Bizhong Xia … Feng ZhengIEEE Trans. Multimedia · University Town of Shenzhen · Tsinghua University · +2
  11. 2023
    Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual LearningKanghao Chen, Sijia Lin, Jianguo Zhang … Ruixuan WangChina Automation Congress (CAC) · Sun Yat-sen University · Southern University of Science and Technology
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  12. 2023
    Multi-Label Classification via Adaptive Resonance Theory-Based ClusteringNaoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao IshibuchiTPAMI · Osaka Metropolitan University · University of Malaya · +1
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  13. 2022
    Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual LearningNaoki Masuyama, Narito Amako, Yuna Yamada … Hisao IshibuchiIEEE Access · Osaka Metropolitan University · Osaka Prefecture University · +1
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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. 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.