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.

10 papers of 8,653Sort Recent · Most cited
  1. 2023
    Is forgetting less a good inductive bias for forward transfer?Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan ChaudhryICLR
    PDF ↗
  2. 2023
    NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchJörg Bornschein, Alexandre Galashov, Ross Hemsley … Marc’Aurelio RanzatoJMLR
    PDF ↗
  3. 2022
    Architecture Matters in Continual LearningSeyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin … Mehrdad FarajtabararXiv
    PDF ↗
  4. 2022
    Wide Neural Networks Forget Less CatastrophicallySeyed Iman Mirzadeh, Arslan Chaudhry, Yin, Dong … Mehrdad FarajtabarICML · Google (United States)
    PDF ↗
  5. 2021
    Using Hindsight to Anchor Past Knowledge in Continual LearningArslan Chaudhry, Albert Gordo, Puneet K. Dokania … David López-PazAAAI · University of Oxford · Meta (Israel)
    PDF ↗
  6. 2020
    Continual Learning in Low-rank Orthogonal SubspacesArslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. TorrNeurIPS · University of Oxford
    PDF ↗
  7. 2019
    On Tiny Episodic Memories in Continual LearningArslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny … M. RanzatoPreprint
  8. 2019
    Continual Learning with Tiny Episodic MemoriesArslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny … Marc’Aurelio RanzatoarXiv · University of Oxford
    PDF ↗
  9. 2019
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
    PDF ↗
  10. 2018
    Riemannian Walk for Incremental Learning: Understanding Forgetting and IntransigenceArslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, Philip H. S. TorrECCV · University of Oxford
    PDF ↗
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.