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. 2020
    Overcoming Catastrophic Forgetting via Direction-Constrained OptimizationYunfei Teng, Anna Choromanska, Murray Campbell … Lior HoreshSpringer LNCS · New York University
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  2. 2020
    Active Class Incremental Learning for Imbalanced DatasetsEden Belouadah, Adrian Popescu, Umang Aggarwal, Léo SaciSpringer LNCS · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +1
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  3. 2020
    Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection MasksMatsumoto Asato, ‪Keiji Yanai‬Springer LNCS · University of Electro-Communications
  4. 2020
    An Incremental Learning Network Model Based on Random Sample Distribution FittingWencong Wang, Lan Huang, Hao Liu … Kangping WangSpringer LNCS · Jilin University · Jilin Province Science and Technology Department
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.