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. 2024
    Text-Enhanced Data-Free Approach for Federated Class-Incremental LearningMinh Tuan Tran, Trung Le, Xuan-May Le … Dinh PhungCVPR · Australian Regenerative Medicine Institute · Monash University · +1
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  2. 2023
    Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic SegmentationYuwei Yang, Munawar Hayat, Jin Zhao … Yinjie LeiCVPR · Sichuan University · Australian Regenerative Medicine Institute · +1
  3. 2022
    On Generalizing Beyond Domains in Cross-Domain Continual LearningChristian Simon, Masoud Faraki, Yi–Hsuan Tsai … Manmohan ChandrakerCVPR · Australian National University · Australian Regenerative Medicine Institute · +5
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  4. 2021
    On Learning the Geodesic Path for Incremental LearningChristian Simon, Piotr Koniusz, Mehrtash HarandiCVPR · Australian National University · Commonwealth Scientific and Industrial Research Organisation · +3
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  5. 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
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.