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.

58 papers of 8,653 · showing 51–58Sort Recent · Most cited
  1. 2022
    Lifelong Incremental Reinforcement Learning With Online Bayesian InferenceZhi Wang, Chunlin Chen, Daoyi DongTNNLS · Nanjing University · University of Canberra · +1
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  2. 2019
    Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and SustainabilityYang Yang, Da-Wei Zhou, De‐Chuan Zhan … Yuan JiangKDD · Nanjing University · Rutgers, The State University of New Jersey
  3. 2019
    Towards AutoML in the presence of Drift: first resultsJorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales … Michèle SébagIJCAI · National Institute of Astrophysics, Optics and Electronics · Nanjing University · +2
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  4. 2019
    Label Mapping Neural Networks with Response Consolidation for Class Incremental LearningXu Zhang, Yao Yang, Baile Xu … Qingwei LinarXiv · Nanjing University · Microsoft Research (United Kingdom)
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  5. 2019
    Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic EnvironmentsZhi Wang, Chunlin Chen, Han‐Xiong Li … Tzyh‐Jong TarnIEEE/ASME Transactions on Mechatronics · Nanjing University · Central South University · +3
  6. 2019
    Perception Coordination Network: A Neuro Framework for Multimodal Concept Acquisition and BindingYoulu Xing, Xiaofeng Shi, Furao Shen … Ah‐Hwee TanTNNLS · Anhui University · Nanjing University · +2
  7. 2017
    New Class Adaptation Via Instance Generation in One-Pass Class Incremental LearningYue Zhu, Kai Ming Ting, Zhi‐Hua ZhouICDM · Nanjing University · Federation University
  8. 2014
    Learning with Augmented Class by Exploiting Unlabeled DataQing Da, Yu Yang, Zhi‐Hua ZhouAAAI · Nanjing University
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.