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
    Federated Class Incremental Learning Method With High Accuracy and Extremely Low Communication Cost Based on Broad Learning SystemJie Du, Wenbing Chen, Peng Liu … C L Philip ChenIEEE Transactions · Shenzhen University · University of Electronic Science and Technology of China · +2
  2. 2026
    Mitigating Catastrophic Forgetting With Adaptive Transformer Block Expansion in Federated Fine-TuningYujia Huo, Jianchun Liu, Hongli Xu … Liusheng HuangIEEE Transactions · University of Science and Technology of China · China University of Mining and Technology
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  3. 2026
    Incremental Micro-Expression Recognition: A BenchmarkZhengqin Lai, Xiaopeng Hong, Yabin Wang, Xiaobai LiIEEE Transactions · Harbin Institute of Technology · Zhejiang University
  4. 2026
    Hybrid Offline–Online Learning of Fuzzy Cognitive Maps for Forecasting Nonstationary Streaming Time SeriesX. Liu, Yingjun Zhang, Hui Wang … Baigen CaiIEEE Transactions · Lanzhou Jiaotong University · Beijing Jiaotong University · +1
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  5. 2026
    DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model With Self-Generated Cross-Modal AlignmentK C Lu, Zhehuai Chen, Szu‐Wei Fu … Hung-yi LeeIEEE Transactions · National Taiwan University · National Taipei University · +2
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  6. 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
  7. 2026
    LLKF: Lifelong Learning Kalman Filter with Noise Statistics Continual LearningAndi Lin, Wen‐An Zhang, Ling Shi, Chenlong LiIEEE Transactions · Zhejiang University of Technology · Hong Kong University of Science and Technology · +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.