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.

10 papers of 11,817Sort Recent · Most cited
  1. 2024
    A masking, linkage and guidance framework for online class incremental learningGuoqiang Liang, Zhao-Jie Chen, Shibin Su … Yanning ZhangPattern Recognition
  2. 2024
    Continual learning with high-order experience replay for dynamic network embeddingZhizheng Wang, Yuanyuan Sun, Xiaokun Zhang … Hongfei LinPattern Recognition
  3. 2024
    OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental LearningWenjun Miao, Guansong Pang, Trong T. Nguyen … Xiaolong BaiPattern Recognition
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  4. 2024
    Pseudo-set Frequency Refinement architecture for fine-grained few-shot class-incremental learningZicheng Pan, Weichuan Zhang, Xiaohan Yu … Yongsheng GaoPattern Recognition
  5. 2024
    Large-scale continual learning for ancient Chinese character recognitionYue Xu, Xu-Yao Zhang, Zhao-kui Zhang, Cheng-Lin LiuPattern Recognition
  6. 2024
  7. 2024
    BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object DetectionYu Chen, Liyan Ma, Liping Jing, Jian-hong YuPattern Recognition
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  8. 2024
  9. 2024
    Hyper-feature aggregation and relaxed distillation for class incremental learningRan Wu, Huan-Yu Liu, Zongcheng Yue … Chiu-Wing ShamPattern Recognition
  10. 2024
    Continual learning for cross-modal image-text retrieval based on domain-selective attentionRui Yang, Shuang Wang, Yu Gu … Li-Cheng JiaoPattern Recognition
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.