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.

5 papers of 7,070Sort Recent · Most cited
  1. 2026
    Complementary Asymmetric Representation Learning for Exemplar-Free Class-Incremental LearningRunhang Chen, Xiao‐Yuan Jing, Xiaodong JiaACM Transactions · Wuhan University · Henan University of Engineering · +2
  2. 2024
    Adaptive Knowledge Matching for Exemplar-Free Class-Incremental LearningRunhang Chen, Xiao‐Yuan Jing, Haowen ChenSpringer LNCS · Wuhan University · Guangdong University of Petrochemical Technology · +1
  3. 2024
    Sharpness-aware gradient guidance for few-shot class-incremental learningRunhang Chen, Xiao‐Yuan Jing, Fei Wu, Hao ChenKnowledge-Based Systems · PLA Information Engineering University · Wuhan University · +2
  4. 2024
    Jointly Optimized Classifiers for Few-Shot Class-Incremental LearningSichao Fu, Qinmu Peng, Xiaorui Wang … Xinge YouIEEE TETCI · Huazhong University of Science and Technology · Jingdong (China) · +2
  5. 2023
    Task-specific parameter decoupling for class incremental learningRunhang Chen, Xiao‐Yuan Jing, Fei Wu … Yaru HaoInformation Sciences · Wuhan University · Guangdong University of Petrochemical Technology · +2
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.