Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 5,456Sort Recent · Most cited
  1. 2024
    Plasticity-Optimized Complementary Networks for Unsupervised Continual LearningAlex Gomez-Villa, Bartłomiej Twardowski, Kai Wang, Joost van de WeijerWACV · Universitat Autònoma de Barcelona · Barcelona Supercomputing Center · +1
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  2. 2023
    Density Map Distillation for Incremental Object CountingChenshen Wu, Joost van de WeijerCVPR · Barcelona Supercomputing Center · Computer Vision Center
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  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
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  4. 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
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  5. 2019
    Resource-aware Elastic Swap Random Forest for Evolving Data StreamsDiego Marrón, Eduard Ayguadé, José R. Herrero, Albert BifetarXiv · Barcelona Supercomputing Center · Universitat Politècnica de Catalunya · +1
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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 written 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.