Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

3 papers of 8,653Sort Recent · Most cited
  1. 2023
    Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least SamplingYang Yang, Da-Wei Zhou, De-Chuan Zhan … Jian YangTKDE · Nanjing University of Science and Technology · Southeast University · +2
  2. 2021
    Learning to Classify With Incremental New ClassDa-Wei Zhou, Yang Yang, De-Chuan ZhanTNNLS · Nanjing University · Nanjing University of Science and Technology
  3. 2019
    Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and SustainabilityYang Yang, Da-Wei Zhou, De‐Chuan Zhan … Yuan JiangACM SIGKDD International Conference on Knowledge Discover… · Nanjing University · Rutgers, The State University of New Jersey
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.