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.

4 papers of 8,653Sort Recent · Most cited
  1. 2024
    Analytic Class Incremental Learning for Sound Source Localization With Privacy ProtectionXinyuan Qian, Xianghu Yue, Jiadong Wang … Haizhou LiIEEE Signal Processing Letters · University of Science and Technology Beijing · National University of Singapore · +4
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  2. 2024
    MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental TasksXianghu Yue, Xueyi Zhang, Yiming Chen … Haizhou LiACM International Conference on Multimedia · National University of Singapore · National University of Defense Technology · +5
  3. 2024
    Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental LearningYicheng Meng, J. L. Ping, Jingye Shi, Ruicong Zhi2024 3rd International Conference on Artificial Intellige… · University of Science and Technology Beijing · Beijing Jiaotong University
  4. 2024
    Defying Imbalanced Forgetting in Class Incremental LearningXu Shi-xiong, Gaofeng Meng, Xing Nie … Shiming XiangAAAI · Chinese Academy of Sciences · Shandong Institute of Automation · +3
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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.