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.

16 papers of 8,653Sort Recent · Most cited
  1. 2023
    Grassmann Graph Embedding for Few-Shot Class Incremental LearningZiqi Gu, Chunyan Xu, Zhen CuiSpringer LNCS · Nanjing University of Science and Technology
  2. 2023
    An ANN-Guided Approach to Task-Free Continual Learning with Spiking Neural NetworksJ. S. Zhang, Wentao Fan, Xin LiuSpringer LNCS · Huaqiao University · Beijing Normal-Hong Kong Baptist University · +2
  3. 2023
    Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse NetworksGhada Sokar, Decebal Constantin Mocanu, Mykola PechenizkiySpringer LNCS · Eindhoven University of Technology · University of Twente
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  4. 2023
    Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech RecognitionTheresa Pekarek Rosin, Stefan WermterSpringer LNCS · Universität Hamburg · Hamburg University of Technology
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  5. 2023
    Generalising via Meta-Examples for Continual Learning in the WildAlessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea PasseriniSpringer LNCS · University of Trento · University of Modena and Reggio Emilia
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  6. 2023
    Recent Advances in Class-Incremental LearningDejie Yang, Minghang Zheng, Weishuai Wang … Yang LiuSpringer LNCS · Peking University
  7. 2023
    Class Incremental Learning with Important and Diverse MemoryLi Mei, Zeyu Yan, Changsheng LiSpringer LNCS · Beijing Institute of Technology
  8. 2023
    Employing Convolutional Neural Networks for Continual LearningMarcin Jasiński, Michał WoźniakSpringer LNCS · Wrocław University of Science and Technology · AGH University of Krakow
  9. 2023
    FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image ClassificationMarkus Weißflog, Peter Protzel, Peer NeubertSpringer LNCS · Chemnitz University of Technology · Koblenz University of Applied Sciences · +1
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  10. 2023
    Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VDanai Koutra, Claudia Plant, Manuel Gomez-Rodriguez … Francesco BonchiSpringer LNCS · University of Michigan · University of Vienna · +2
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  11. 2023
    Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANsZeren Zhang, Xingjian Li, Hong Tao … Chengzhong XuSpringer LNCS · Peking University · Baidu (China) · +2
  12. 2023
    POSTER: Advancing Federated Edge Computing with Continual Learning for Secure and Efficient PerformanceChunlu Chen, Kevin I‐Kai Wang, Peng Li, Kouichi SakuraiSpringer LNCS · Kyushu University · University of Auckland · +1
  13. 2023
    Dynamic Memory-Based Continual Learning with Generating and ScreeningSiying Tao, Jinyang Huang, Xiang Zhang … Yu GuSpringer LNCS · Hefei University of Technology · University of Science and Technology of China · +1
  14. 2023
    NeCa: Network Calibration for Class Incremental LearningZhenyao Zhang, Lijun ZhangSpringer LNCS · Nanjing University
  15. 2023
    Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action LocalizationTianquan Chen, Bairong Li, Yusheng Tao … Yuesheng ZhuSpringer LNCS · Peking University
  16. 2023
    Continual Vocabularies to Tackle the Catastrophic Forgetting Problem in Machine TranslationSalvador Carrión, Francisco CasacubertaSpringer LNCS · Universitat Politècnica de València
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.