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.

15 papers of 8,653Sort Recent · Most cited
  1. 2026
    Vision-Language Efficient Tuning for Mitigating Catastrophic Forgetting in Multi-Modal LearningYaoming Wang, Yuchen Liu, Wenrui Dai … Hongkai XiongIJCV · Shanghai Jiao Tong University · Huawei Technologies (China)
  2. 2026
    Why not transform chat large language models to non-English?Xiang Geng, Ming Zhu, Jiahuan Li … Shujian HuangFrontiers of Computer Science · Nanjing University · Huawei Technologies (China)
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  3. 2026
    Adaptive Quantization for Stable Knowledge Acquisition in Quantization-Aware Continual LearningDe Cheng, Kun Gu, Lingfeng He … Xinbo GaoIEEE TCSVT · Xidian University · Huawei Technologies (China)
  4. 2025
    Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language ModelsDawei Pan, Zhaoyang Fu, Jingyuan Wang … Xiangyu ZhaoCIKM · City University of Hong Kong · Beihang University · +2
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  5. 2024
    Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental LearningXiang Song, Kuang Shu, Songlin Dong … Yihong GongWACV · Xi'an Jiaotong University · China Power Engineering Consulting Group (China) · +1
  6. 2023
    Decomposing Logits Distillation for Incremental Named Entity RecognitionDuzhen Zhang, Yahan Yu, Feilong Chen, Xiuyi ChenSIGIR · Baidu (China) · Huawei Technologies (China)
  7. 2023
    DKT: Diverse Knowledge Transfer Transformer for Class Incremental LearningXinyuan Gao, Yuhang He, Songlin Dong … Yihong GongCVPR · Xi'an Jiaotong University · Huawei Technologies (China)
  8. 2023
    Learnable Distribution Calibration for Few-Shot Class-Incremental LearningBinghao Liu, Boyu Yang, Lingxi Xie … Qixiang YeTPAMI · University of Chinese Academy of Sciences · Huawei Technologies (China)
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  9. 2024
    Deep Class-Incremental Learning From Decentralized DataXiaohan Zhang, Songlin Dong, Jinjie Chen … Xiaopeng HongTNNLS · Xi'an Jiaotong University · Huawei Technologies (China) · +1
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  10. 2020
    MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental LearningHanbin Zhao, Yongjian Fu, Mintong Kang … Xi LiTPAMI · Zhejiang University of Science and Technology · Huawei Technologies (China)
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  11. 2020
    GraphSAIL: Graph Structure Aware Incremental Learning for Recommender SystemsYishi Xu, Yingxue Zhang, Wei Guo … Mark CoatesCIKM · Université de Montréal · Huawei Technologies (Canada) · +2
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  12. 2020
    Semantic Drift Compensation for Class-Incremental LearningLu Yu, Bartłomiej Twardowski, Xialei Liu … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Northwestern Polytechnical University · +1
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  13. 2020
    Generative Feature Replay For Class-Incremental LearningXialei Liu, Chenshen Wu, Mikel Menta … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · University of Florence · +1
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  14. 2020
    More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental LearningYu Liu, Sarah Parisot, Greg Slabaugh … Tinne TuytelaarsECCV · KU Leuven · Huawei Technologies (China) · +1
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  15. 2019
    Is Fast Adaptation All You Need?Khurram Javed, Hengshuai Yao, Martha WhitearXiv · University of Alberta · Huawei Technologies (China)
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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.