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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    PIC-CMH: Efficient Prompt-Infused Continual Cross-Modal HashingFengling Li, Wenhao Liu, Tianshi Wang … Xiaojun ChangIEEE Trans. Multimedia · University of Technology Sydney · Shandong Normal University · +1
  2. 2025
    Fast Partial-Modal Online Cross-Modal HashingFengling Li, Yang Sun, Tianshi Wang … Xiaojun ChangTIP · University of Technology Sydney · Shandong Normal University · +1
  3. 2021
    One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic ForgettingMiao Zhang, Huiqi Li, Shirui Pan … Steven W. SuTPAMI · Beijing Institute of Technology · Monash University · +2
  4. 2020
    Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationMiao Zhang, Huiqi Li, Shirui Pan … Steven W. SuCVPR · University of Technology Sydney · Beijing Institute of Technology · +2
  5. 2019
    Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-CorrectionFengda Zhu, Xiaojun Chang, Runhao Zeng, Mingkui TanarXiv · South China University of Technology
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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.