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.

10 papers of 7,070Sort Recent · Most cited
  1. 2025
    Amplitude-aware Domain Style Replay for Lifelong Person Re-identificationLong Chen, De Cheng, Shizhou Zhang … Yanning ZhangACM MM
  2. 2025
  3. 2024
  4. 2024
    FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph LearningJinhui Pang, Changqing Lin, Xiaoshuai Hao … Taisheng HuangACM MM · Beijing Institute of Technology · Samsung (China) · +2
    PDF ↗
  5. 2024
    Class Balance Matters to Active Class-Incremental LearningZhenhong Huang, Ze Chen, Yuanze Li … Wangmeng ZuoACM MM · Harbin Institute of Technology · Megvii (China) · +2
    PDF ↗
  6. 2023
    CUCL: Codebook for Unsupervised Continual LearningCheng Chen, Jingkuan Song, Xiaosu Zhu … Heng Tao ShenACM MM · University of Electronic Science and Technology of China
    PDF ↗
  7. 2023
    FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object LocalizationSejin Park, Tae‐Hyung Lee, Yeejin Lee, Byeongkeun KangACM MM · Seoul National University of Science and Technology
    PDF ↗
  8. 2023
    Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge AnchorJinqian Chen, Jihua Zhu, Qinghai ZhengACM MM · Xi'an Jiaotong University · Fuzhou University
    PDF ↗
  9. 2021
    Structural Knowledge Organization and Transfer for Class-Incremental LearningYu Liu, Xiaopeng Hong, Xiaoyu Tao … Yihong GongACM MM · Xi'an Jiaotong University
  10. 2021
    CoReD: Generalizing Fake Media Detection with Continual Representation using DistillationMinha Kim, Shahroz Tariq, Simon S. WooACM MM · Sungkyunkwan University
    PDF ↗
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.