Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

127 papers of 6,984 · showing 51–100Sort Recent · Most cited
  1. 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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  2. 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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  3. 2023
    Recent Advances in Class-Incremental LearningDejie Yang, Minghang Zheng, Weishuai Wang … Yang LiuSpringer LNCS · Peking University
  4. 2020
    Overcoming Catastrophic Forgetting via Direction-Constrained OptimizationYunfei Teng, Anna Choromanska, Murray Campbell … Lior HoreshSpringer LNCS · New York University
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  5. 2023
    Class Incremental Learning with Important and Diverse MemoryLi Mei, Zeyu Yan, Changsheng LiSpringer LNCS · Beijing Institute of Technology
  6. 2023
    Employing Convolutional Neural Networks for Continual LearningMarcin Jasiński, Michał WoźniakSpringer LNCS · Wrocław University of Science and Technology · AGH University of Krakow
  7. 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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  8. 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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  9. 2023
    Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANsZeren Zhang, Xingjian Li, Hong Tao … Chengzhong XuSpringer LNCS · Peking University · Baidu (China) · +2
  10. 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
  11. 2023
    Using Flexible Memories to Reduce Catastrophic ForgettingWernsen Wong, Yun Sing Koh, Gillian DobbieSpringer LNCS · University of Auckland
  12. 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
  13. 2023
    NeCa: Network Calibration for Class Incremental LearningZhenyao Zhang, Lijun ZhangSpringer LNCS · Nanjing University
  14. 2023
    Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action LocalizationTianquan Chen, Bairong Li, Yusheng Tao … Yuesheng ZhuSpringer LNCS · Peking University
  15. 2023
    Continual Vocabularies to Tackle the Catastrophic Forgetting Problem in Machine TranslationSalvador Carrión, Francisco CasacubertaSpringer LNCS · Universitat Politècnica de València
  16. 2022
    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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  17. 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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  18. 2022
    Unsupervised Continual Learning Via Pseudo LabelsJiangpeng He, Fengqing ZhuSpringer LNCS · Purdue University West Lafayette
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  19. 2022
    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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  20. 2022
    SPeCiaL: Self-Supervised Pretraining for Continual LearningLucas Caccia, Joëlle PineauSpringer LNCS · McGill University
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  21. 2022
    Knowledge Lock: Overcoming Catastrophic Forgetting in Federated LearningGuoyizhe Wei, Xiu LiSpringer LNCS · University Town of Shenzhen · Tsinghua University
  22. 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
  23. 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
  24. 2022
    Adaptive Online Domain Incremental Continual LearningNuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard PfahringerSpringer LNCS · University of Waikato
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  25. 2022
    Contrastive Supervised Distillation for Continual Representation LearningTommaso Barletti, Niccolò Biondi, Federico Pernici … Alberto Del BimboSpringer LNCS · University of Florence
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  26. 2022
    Avalanche RL: a Continual Reinforcement Learning LibraryNicoló Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide BacciuSpringer LNCS · University of Pisa
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  27. 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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  28. 2022
    Adaptive Feature Generation for Online Continual Learning from Imbalanced DataYingchun Jian, Jinfeng Yi, Lijun ZhangSpringer LNCS · Nanjing University · Jingdong (China)
  29. 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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  30. 2022
    Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement LearningRobin Schiewer, Laurenz WiskottSpringer LNCS · Ruhr University Bochum
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  31. 2022
    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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  32. 2022
    Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in TransformersPhilip Kenneweg, Alexander Schulz, Sarah Schröder, Barbara HammerSpringer LNCS · Bielefeld University
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  33. 2022
    Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual LearningTaeheon Kim, Hyung-Jun Moon, Sung‐Bae ChoSpringer LNCS · Yonsei University
  34. 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
  35. 2022
    Catastrophic Forgetting in Continual Concept Bottleneck ModelsEmanuele Marconato, Gianpaolo Bontempo, Stefano Teso … Andrea PasseriniSpringer LNCS · University of Pisa · University of Trento · +1
  36. 2022
    Continual Learning with Neuron Activation ImportanceSohee Kim, Seungkyu LeeSpringer LNCS · Kyung Hee University
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  37. 2022
    Modular-Relatedness for Continual LearningAmmar Shaker, Francesco Alesiani, Shujian YuSpringer LNCS · UiT The Arctic University of Norway
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  38. 2022
    Transfer and Continual Supervised Learning for Robotic Grasping Through Grasping FeaturesLuca Monorchio, Marco Capotondi, Mario Corsanici … Francesco PujaSpringer LNCS · Sapienza University of Rome
  39. 2022
    Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary ClassificationJingbo Sun, Li Yang, Jiaxin Zhang … Yu CaoSpringer LNCS · Arizona State University · Oak Ridge National Laboratory · +1
  40. 2022
    Continual Learning Based on Knowledge Distillation and Representation LearningXiuyan Chen, Jian–wei Liu, Wentao LiSpringer LNCS · China University of Petroleum, Beijing
  41. 2022
    On Regret Bounds for Continual Single-Index LearningThe Tien MaiSpringer LNCS · Norwegian University of Science and Technology
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  42. 2022
    Partially Relaxed Masks for Knowledge Transfer Without Forgetting in Continual LearningTatsuya Konishi, Mori Kurokawa, Chihiro Ono … Bing LiuSpringer LNCS · KDDI Research (Japan) · University of Illinois Chicago
  43. 2021
    Continual Learning with Knowledge Transfer for Sentiment ClassificationZixuan Ke, Bing Liu, Hao Wang, Lei ShuSpringer LNCS · University of Illinois Chicago · Southwest Jiaotong University
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  44. 2021
    Studying Catastrophic Forgetting in Neural Ranking ModelsJesús Lovón-Melgarejo, Laure Soulier, Karen Pinel-Sauvagnat, Lynda TamineSpringer LNCS · Université Toulouse III - Paul Sabatier · Institut de Recherche en Informatique de Toulouse · +4
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  45. 2021
    Learning without Forgetting for 3D Point Cloud ObjectsTownim Faisal Chowdhury, Mahira Jalisha, Ali Cheraghian, Shafin RahmanSpringer LNCS · North South University · Australian National University · +2
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  46. 2021
    Continual Learning with Dual RegularizationsXuejun Han, Yuhong GuoSpringer LNCS · Carleton University · Canadian Institute for Advanced Research
  47. 2021
    Explaining How Deep Neural Networks Forget by Deep VisualizationGiang V. Nguyen, Chen Shuan, Tae Joon Jun, Daeyoung KimSpringer LNCS · Korea Advanced Institute of Science and Technology · Ansan University
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  48. 2021
    DRILL: Dynamic Representations for Imbalanced Lifelong LearningKyra Ahrens, Fares Abawi, Stefan WermterSpringer LNCS · Universität Hamburg
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  49. 2021
    Class-incremental Learning with Rectified Feature-Graph PreservationCheng-Hsun Lei, Yi-Hsin Chen, Wen-Hsiao Peng, Wei-Chen ChiuSpringer LNCS · National Yang Ming Chiao Tung University
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  50. 2021
    Continual Learning with Laplace Operator Based Node-Importance Dynamic Architecture Neural NetworkZhiyuan Li, Ming Meng, Yifan He, Yihao LiaoSpringer LNCS · Hangzhou Dianzi 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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.