Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 6,984Sort Recent · Most cited
  1. 2019
    Incremental learning for the detection and classification of GAN-generated imagesFrancesco Marra, Cristiano Saltori, Giulia Boato, Luisa VerdolivaInternational Workshop on Information Forensics and Security · Federico II University Hospital · University of Trento
    PDF ↗
  2. 2019
    Learning to Remember: A Synaptic Plasticity Driven Framework for Continual LearningOleksiy Ostapenko, Mihai Puscas, Tassilo Klein … Moin NabiCVPR · Humboldt-Universität zu Berlin · University of Trento · +1
    PDF ↗
  3. 2019
    Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question AnsweringClaudio Greco, Barbara Plank, Raquel Fernández, Raffaella BernardiACL · University of Trento · IT University of Copenhagen · +1
    PDF ↗
  4. 2019
    Adding New Tasks to a Single Network with Weight Trasformations using Binary MasksMassimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota BulòSpringer LNCS · Fondazione Bruno Kessler · Sapienza University of Rome · +2
    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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.