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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    Multimodal Deepfake Detection with Quantum State Inspired Analytic Incremental Adaptability LearningJianbin Ye, Man Xiao, Bo Liu … Huaimin Wang2026 International Conference on Multimedia Retrieval · National University of Defense Technology
  2. 2026
    We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series ClassificationZhipeng Liu, Peibo Duan, Xuan Tang … Binwu WangACM Web Conference 2026 · Northeastern University · Xi'an Jiaotong University · +2
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  3. 2026
    KSS-MoE: Knowledge Space Synergy Framework in Mixture of Experts for Continual Visual Instruction TuningLingyun Song, Ziyao Chen, Kang Pan … Xuequn ShangAAAI · Northwestern Polytechnical University · San Francisco Bay University · +1
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  4. 2026
    PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental LearningXianghu Yue, Yiming Chen, Xueyi Zhang … Haizhou LiPattern Recognition · Tianjin University · National University of Singapore · +4
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  5. 2026
    Rep Deep & Machine Learning: Exemplar-Free Continual Video Action Recognition via Slow-Fast Collaborative LearningXueyi Zhang, Jianhua Zhang, Zheng Li … Huiping ZhuangAAAI · Chinese University of Hong Kong, Shenzhen · University of Chinese Academy of Sciences · +4
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  6. 2026
    CEFCIL: Comprehensive Ensemble Framework for Exemplar-Free Class Incremental LearningWei Liu, Cheng Zhu, Fan Wu … Q. LiuBig Data Mining and Analytics · National University of Defense Technology · Central South University
  7. 2026
    Adaptive Affinity Memorization With Layer Mutation for Multimodal Deepfake Continual DetectionMan Xiao, Jianbin Ye, Bo Liu … Kele XuIEEE Transactions · National University of Defense Technology · Hunan Normal 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.