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. 2021
    Static-Dynamic Co-Teaching for Class-Incremental 3D Object DetectionNa Zhao, Gim Hee LeeAAAI · National University of Singapore
    PDF ↗
  2. 2021
    Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental LearningYujun Shi, Kuangqi Zhou, Jian Liang … Vincent Y. F. TanCVPR · National University of Singapore · Chinese Academy of Sciences · +1
    PDF ↗
  3. 2021
    Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic DataKuluhan Binici, Nam Trung Pham, Tulika Mitra, Karianto LemanWACV · Agency for Science, Technology and Research · National University of Singapore · +1
    PDF ↗
  4. 2021
    Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object DetectionNa Dong, Yongqiang Zhang, Mingli Ding, Gim Hee LeeNeurIPS · Harbin Institute of Technology · National University of Singapore
    PDF ↗
  5. 2021
    GarbageNet: A Unified Learning Framework for Robust Garbage ClassificationJianfei Yang, Zhaoyang Zeng, Kai Wang … Lihua XieIEEE TAI · Nanyang Technological University · Sun Yat-sen University · +2
  6. 2021
    Continual Learning via Bit-Level Information PreservingYujun Shi, Li Yuan, Yunpeng Chen, Jiashi FengCVPR · National University of Singapore
    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 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.