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. 2025
    Overcoming Dual Drift for Continual Long-Tailed Visual Question AnsweringFeifei Zhang, Zhihao Wang, Xi Zhang, Changsheng XuICCV · Tianjin University of Technology · Alibaba Group (United States) · +1
  2. 2025
    Leveraging Multiple Deep Experts for Online Class-incremental LearningZhe Tao, Lu Yu, Hantao Yao, Changsheng XuIEEE International Conference on Multimedia and Expo (ICME) · Tianjin University of Technology · University of Science and Technology of China · +2
  3. 2025
    Language Guided Concept Bottleneck Models for Interpretable Continual LearningLu Yu, Haoyu Han, Zhe Tao … Chris XuCVPR · Tianjin University of Technology · University of Science and Technology of China · +2
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  4. 2025
    Dual Uncertainty-Aware Correspondence Adapting and Retaining for Continual Composed Image RetrievalHaoliang Zhou, Feifei Zhang, Changsheng XuTIP · Tianjin University of Technology · Chinese Academy of Sciences · +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.