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.

229 papers of 8,653 · showing 101–150Sort Recent · Most cited
  1. 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
  2. 2023
    Online Class-Incremental Learning in Image Classification Based on AttentionBaoyu Du, Zhonghe Wei, Jinyong Cheng … Xiaoyu DaiSpringer LNCS · Qilu University of Technology · Shandong University · +1
  3. 2021
    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. 2026
    cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time SeriesFederico Giannini, Giacomo Ziffer, Emanuele Della ValleSpringer LNCS · Politecnico di Milano
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  5. 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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  6. 2023
    Evolutionary FPGA-Based Spiking Neural Networks for Continual LearningA. Otero, Guillermo Sanllorente, Eduardo de la Torre, Jose Nunez‐YanezSpringer LNCS · Universidad Politécnica de Madrid · Linköping University
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  7. 2023
    Recent Advances in Class-Incremental LearningDejie Yang, Minghang Zheng, Weishuai Wang … Yang LiuSpringer LNCS · Peking University
  8. 2021
    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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  9. 2020
    Overcoming Catastrophic Forgetting via Direction-Constrained OptimizationYunfei Teng, Anna Choromanska, Murray Campbell … Lior HoreshSpringer LNCS · New York University
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  10. 2024
    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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  11. 2023
  12. 2023
    Class Incremental Learning with Important and Diverse MemoryLi Mei, Zeyu Yan, Changsheng LiSpringer LNCS · Beijing Institute of Technology
  13. 2023
    Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANsZeren Zhang, Xingjian Li, Hong Tao … Chengzhong XuSpringer LNCS · Peking University · Baidu (China) · +2
  14. 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
  15. 2023
    Using Flexible Memories to Reduce Catastrophic ForgettingWernsen Wong, Yun Sing Koh, Gillian DobbieSpringer LNCS · University of Auckland
  16. 2022
  17. 2022
    Employing Convolutional Neural Networks for Continual LearningMarcin Jasiński, Michał WoźniakSpringer LNCS · Wrocław University of Science and Technology · AGH University of Krakow
  18. 2022
    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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  19. 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
  20. 2023
    NeCa: Network Calibration for Class Incremental LearningZhenyao Zhang, Lijun ZhangSpringer LNCS · Nanjing University
  21. 2023
    Continual Vocabularies to Tackle the Catastrophic Forgetting Problem in Machine TranslationSalvador Carrión, Francisco CasacubertaSpringer LNCS · Universitat Politècnica de València
  22. 2022
    Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action LocalizationTianquan Chen, Bairong Li, Yusheng Tao … Yuesheng ZhuSpringer LNCS · Peking University
  23. 2023
    S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental LearningJayateja Kalla, Soma BiswasSpringer LNCS · Indian Institute of Science Bangalore
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  24. 2021
    Distilled Replay: Overcoming Forgetting through Synthetic SamplesAndrea Rosasco, Antonio Carta, Andrea Cossu … Davide BacciuSpringer LNCS · University of Pisa · Scuola Normale Superiore
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  25. 2022
    Helpful or Harmful: Inter-task Association in Continual LearningHyundong Jin, Eunwoo KimSpringer LNCS · Chung-Ang University
  26. 2022
    Practical Recommendations for Replay-based Continual Learning MethodsGabriele Merlin, Vincenzo Lomonaco, Andrea Cossu … Davide BacciuSpringer LNCS · University of Pisa · Scuola Normale Superiore
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  27. 2021
    Unsupervised Continual Learning Via Pseudo LabelsJiangpeng He, Fengqing ZhuSpringer LNCS · Purdue University West Lafayette
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  28. 2022
    Online Task-free Continual Learning with Dynamic Sparse Distributed MemoryJulien Pourcel, Ngoc‐Son Vu, Robert M. FrenchSpringer LNCS · Centre National de la Recherche Scientifique · Equipes Traitement de l'Information et Systèmes · +1
  29. 2021
    Unsupervised Continual Learning via Self-Adaptive Deep Clustering ApproachMahardhika Pratama, Andri Ashfahani, Edwin LughoferSpringer LNCS · University of South Australia · Nanyang Technological University
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  30. 2021
    SPeCiaL: Self-Supervised Pretraining for Continual LearningLucas Caccia, Joëlle PineauSpringer LNCS · McGill University
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  31. 2022
    incDFM: Incremental Deep Feature Modeling for Continual Novelty DetectionAmanda Rios, Nilesh Ahuja, Ibrahima J. Ndiour … Omesh TickooSpringer LNCS · University of Southern California · Intel (United States)
  32. 2022
    Knowledge Lock: Overcoming Catastrophic Forgetting in Federated LearningGuoyizhe Wei, Xiu LiSpringer LNCS · University Town of Shenzhen · Tsinghua University
  33. 2021
    Coarse-To-Fine Incremental Few-Shot LearningXiang Xiang, Yuwen Tan, Qian Wan … Gregory D. HagerSpringer LNCS · Huazhong University of Science and Technology · Johns Hopkins University
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  34. 2022
    Balancing Between Forgetting and Acquisition in Incremental Subpopulation LearningMingfu Liang, Jiahuan Zhou, Wei Wei, Ying WuSpringer LNCS · Northwestern University · Peking University
  35. 2022
    Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture ApproximationHoang Phan, Anh Phan Tuan, Son Nguyen … Khoat ThanSpringer LNCS · VinUniversity · Hanoi University of Science and Technology
  36. 2022
    Auxiliary Local Variables for Improving Regularization/Prior Approach in Continual LearningLinh Ngo Van, Nam Le Hai, Hoang Pham, Khoat ThanSpringer LNCS · Hanoi University of Science and Technology
  37. 2022
    Adaptive Online Domain Incremental Continual LearningNuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard PfahringerSpringer LNCS · University of Waikato
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  38. 2022
    Contrastive Supervised Distillation for Continual Representation LearningTommaso Barletti, Niccolò Biondi, Federico Pernici … Alberto Del BimboSpringer LNCS · University of Florence
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  39. 2022
    Avalanche RL: a Continual Reinforcement Learning LibraryNicoló Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide BacciuSpringer LNCS · University of Pisa
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  40. 2022
    Continual Learning of Long Topic Sequences in Neural Information Retrieval - abstractThomas Gerald, Laure SoulierSpringer LNCS · Centre National de la Recherche Scientifique · Sorbonne Université · +1
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  41. 2022
    Adaptive Feature Generation for Online Continual Learning from Imbalanced DataYingchun Jian, Jinfeng Yi, Lijun ZhangSpringer LNCS · Nanjing University · Jingdong (China)
  42. 2022
    Adaptive Neural Networks for Online Domain Incremental Continual LearningNuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard PfahringerSpringer LNCS · University of Waikato
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  43. 2021
    Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement LearningRobin Schiewer, Laurenz WiskottSpringer LNCS · Ruhr University Bochum
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  44. 2024
    Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in TransformersPhilip Kenneweg, Alexander Schulz, Sarah Schröder, Barbara HammerSpringer LNCS · Bielefeld University
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  45. 2021
    Evaluating Continual Learning Algorithms by Generating 3D Virtual EnvironmentsEnrico Meloni, Alessandro Betti, Lapo Faggi … Stefano MelacciSpringer LNCS · University of Siena · University of Florence
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  46. 2022
    Computationally Efficient Rehearsal for Online Continual LearningCharalampos Davalas, Dimitrios Michail, Christos Diou … Konstantinos TserpesSpringer LNCS · Harokopio University of Athens
  47. 2022
    Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual LearningTaeheon Kim, Hyung-Jun Moon, Sung‐Bae ChoSpringer LNCS · Yonsei University
  48. 2022
    A Novel Continual Learning Approach for Competitive Neural NetworksEsteban J. Palomo, Juan Miguel Ortiz-de-Lazcano-Lobato, José David Fernández-Rodríguez … Rosa Maza-QuirogaSpringer LNCS · Instituto de Investigación Biomédica de Málaga · Universidad de Málaga
  49. 2022
    Catastrophic Forgetting in Continual Concept Bottleneck ModelsEmanuele Marconato, Gianpaolo Bontempo, Stefano Teso … Andrea PasseriniSpringer LNCS · University of Pisa · University of Trento · +1
  50. 2022
    Real Time Data Augmentation Using Fractional Linear Transformations in Continual LearningArijit PatraSpringer LNCS · Biopharma Technology (United Kingdom)
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.