Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 11,817Sort Recent · Most cited
  1. 2022
    Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic SegmentationDipam Goswami, René Schuster, Joost van de Weijer, Didier StrickerWACV · German Research Centre for Artificial Intelligence · Birla Institute of Technology and Science, Pilani · +2
    PDF ↗
  2. 2020
    Class-Incremental Learning: Survey and Performance Evaluation on Image ClassificationMarc Masana, Xialei Liu, Bartłomiej Twardowski … Joost van de WeijerTPAMI · Computer Vision Center
    PDF ↗
  3. 2022
    Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight RegularizationFrancesco Pelosin, Saurav Jha, Andrea Torsello … Joost van de WeijerCVPR · Ca' Foscari University of Venice · UNSW Sydney · +2
    PDF ↗
  4. 2021
    ACAE-REMIND for Online Continual Learning with Compressed Feature ReplayKai Wang, Joost van de Weijer, Luis HerranzPattern Recognition Letters · Universitat Autònoma de Barcelona · Computer Vision Center
    PDF ↗
  5. 2021
    Ternary Feature Masks: zero-forgetting for task-incremental learningMarc Masana, Tinne Tuytelaars, Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Barcelona Supercomputing Center · +2
    PDF ↗
  6. 2018
    Rotate your Networks: Better Weight Consolidation and Less Catastrophic ForgettingXialei Liu, Marc Masana, Luis Herranz … Andrew D. BagdanovICPR · Computer Vision Center · University of Florence
    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 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.