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. 2026PDF ↗
  2. 2026
    PADP: progressive and adaptive data pruning for efficient incremental learningBiqing Duan, Di Liu, Zhenli He … Shengfa MiaoScientific Reports
  3. 2026
    Self-supervised prototype alignment and aggregation for few-shot class-incremental infrared target recognitionDi Liu, Yan Zhang, Zhiguang Shi … Ruo-Bin GaoOptics & Laser Technology
  4. 2026
    Few-shot class-incremental infrared target recognition via contrastive self-supervised subspace classifierDi Liu, Yan Zhang, Zhiguang Shi … Feng LingOptics & Laser Technology
  5. 2025
    LODAP: On-Device Incremental Learning Via Lightweight Operations and Data PruningBiqing Duan, Qing Wang, Di Liu … Shengfa MiaoJournal of systems architecture
    PDF ↗
  6. 2020
    A Hybrid Recursive Implementation of Broad Learning With Incremental FeaturesDi Liu, Simone Baldi, Wenwu Yu, C. L. Philip ChenTNNLS · Southeast University · Delft University of Technology · +1
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.