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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    Tabular continual learning from high-heterogeneity feature spaces via memory and forgetting dual-drivenYan Xian, Hong Yu, Yongfang Xie, Guoyin WangInformation Processing & Management · Chongqing University of Posts and Telecommunications · Central South University · +1
  2. 2025
    Generating samples for covariance to update prototype in few-shot class-incremental learningHong Yu, Qiwei Luo, Ye Wang, Guoyin WangApplied Intelligence · Chongqing University of Posts and Telecommunications · Chongqing Normal University
  3. 2025
    Exploring multi-granularity balance strategy for class incremental learning via three-way granular computingYan Xian, Hong Yu, Ye Wang, Guoyin WangBrain Informatics · Chongqing University of Posts and Telecommunications · Chongqing Normal University
  4. 2025
    A Novel Class Incremental Learning Method via Multi-granularity Balance Inspired by Human Granular Cognition MechanismYan Xian, Hong Yu, Ye Wang, Guoyin WangSpringer LNCS · Chongqing University of Posts and Telecommunications · Chongqing University · +1
  5. 2024
    Class Incremental Learning via Semantic Information Mapping and Background Information CalibratingYan Xian, Hong Yu, Huaxiong Li, Guoyin WangIEEE TCSVT · Chongqing University of Posts and Telecommunications · Nanjing University
  6. 2024
    Open Continual Feature Selection via Granular-Ball Knowledge TransferXuemei Cao, Xin Yang, Shuyin Xia … Tianrui LiTKDE · Southwestern University of Finance and Economics · Chongqing University of Posts and Telecommunications · +1
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  7. 2018
    Generative Adversarial Network Training is a Continual Learning ProblemKevin J Liang, Chunyuan Li, Guoyin Wang, Lawrence CarinarXiv
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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. By default it shows the 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. The rest are one click away under “All papers”. 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.