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.

8 papers of 11,817Sort Recent · Most cited
  1. 2021
    Quantum Continual Learning Overcoming Catastrophic ForgettingWenjie Jiang, Zhide Lu, Dong-Ling DengChinese Physics Letters · Tsinghua University · ShangHai JiAi Genetics & IVF Institute
    PDF ↗
  2. 2021
    Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationTiantian Zhang, Xueqian Wang, Bin Liang, Bo YuanTNNLS · University Town of Shenzhen · Tsinghua University
    PDF ↗
  3. 2021
    AFEC: Active Forgetting of Negative Transfer in Continual LearningLiyuan Wang, Ming‐Tian Zhang, Zhongfan Jia … Yi ZhongNeurIPS · Tsinghua University · University College London
    PDF ↗
  4. 2021
    Memory Recall: A Simple Neural Network Training Framework Against Catastrophic ForgettingBaosheng Zhang, Yuchen Guo, Yipeng Li … Qionghai DaiTNNLS · Tsinghua University · Tsinghua–Berkeley Shenzhen Institute
  5. 2021
    ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual LearningLiyuan Wang, Kuo Yang, Chongxuan Li … Jun ZhuCVPR · Tsinghua University · Huawei Technologies (Sweden)
    PDF ↗
  6. 2021
    Enhanced prototypical network for few-shot relation extractionWen Wen, Yongbin Liu, Chunping Ouyang … Tonglee ChungInformation Processing & Management · Department of Science and Technology of Hunan Province · Tsinghua University
  7. 2021
    A Survey on Curriculum LearningXin Wang, Yudong Chen, Wenwu ZhuTPAMI · Tsinghua University
  8. 2021
    Transfer Learning for Sequence Generation: from Single-source to Multi-sourceXuancheng Huang, Jingfang Xu, Maosong Sun, Yang LiuACL · King Center · Tsinghua University · +2
    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.