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.

38 papers of 8,653Sort Recent · Most cited
  1. 2026
    Forgetting, plasticity, and co-observation: a third facet of continual learningTimm Hess, Abhishek Jha, Gido M. van de Ven, Tinne TuytelaarsarXiv
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  2. 2026
    Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision ModelsSergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen … Tinne TuytelaarsarXiv
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  3. 2026PDF ↗
  4. 2026
    Modular Memory is the Key to Continual Learning AgentsVaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov … Rahaf AljundiarXiv
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  5. 2026
    Putting a Face to Forgetting: Continual Learning meets Mechanistic InterpretabilitySergi Masip, Gido M. van de Ven, Javier Ferrando, Tinne TuytelaarsarXiv
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  6. 2026
    Deep Continual Learning in the Foundation Model Era (Dagstuhl Seminar 25432)Christopher Kanan, Martin Mundt, Tinne Tuytelaars … Timm Felix HessDagstuhl reports · University of Rochester · University of Bremen · +2
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  7. 2025PDF ↗
  8. 2025
    Continual Learning Should Move Beyond Incremental ClassificationRupert Mitchell, Antonio Alliegro, Raffaello Camoriano … Martin MundtarXiv
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  9. 2024
    Learning to Route for Dynamic Adapter Composition in Continual Learning with Language ModelsVladimir Araujo, Marie‐Francine Moens, Tinne TuytelaarsEMNLP
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  10. 2025PDF ↗
  11. 2024
    Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from GeneralizationSebastian Dziadzio, Çağatay Yıldız, Gido M. van de Ven … Matthias BethgeCoLLAs
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  12. 2024
    Continual Learning of Diffusion Models with Generative DistillationSergi Masip, Pau Rodríguez, Tinne Tuytelaars, Gido M. van de VenCoLLAs
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  13. 2024
    Continual Learning: Applications and the Road ForwardEli Verwimp, Rahaf Aljundi, Shai Ben-David … Gido M. van de VenTMLR
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  14. 2023PDF ↗
  15. 2023
    Continual Learning with Pretrained Backbones by Tuning in the Input SpaceSimone Marullo, Matteo Tiezzi, Marco Gori … Tinne TuytelaarsIJCNN · University of Siena · University of Florence · +1
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  16. 2024
    Prediction Error-based Classification for Class-Incremental LearningMichał Zając, Tinne Tuytelaars, Gido M. van de VenICLR
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  17. 2024PDF ↗
  18. 2022
    Three types of incremental learningGido M. van de Ven, Tinne Tuytelaars, Andreas S. ToliasNature Machine Intelligence · Baylor College of Medicine · University of Cambridge · +2
  19. 2022
    Generative Negative Text Replay for Continual Vision-Language PretrainingShipeng Yan, Lanqing Hong, Hang Xu … Xuming HeECCV
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  20. 2023
    CLAD: A realistic Continual Learning benchmark for Autonomous DrivingEli Verwimp, Kuo Yang, Sarah Parisot … Tinne TuytelaarsNeural Networks
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  21. 2023
    Continual evaluation for lifelong learning: Identifying the stability gapMatthias De Lange, Gido M. van de Ven, Tinne TuytelaarsICLR
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  22. 2024
    Continual Pre-Training Mitigates Forgetting in Language and VisionAndrea Cossu, Tinne Tuytelaars, Antonio Carta … Davide BacciuNeural Networks
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  23. 2021PDF ↗
  24. 2021
    Rehearsal revealed: The limits and merits of revisiting samples in continual learningEli Verwimp, Matthias De Lange, Tinne TuytelaarsICCV · KU Leuven
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  25. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  26. 2021
    Ternary Feature Masks: zero-forgetting for task-incremental learningMarc Masana, Tinne Tuytelaars, Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Barcelona Supercomputing Center · +2
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  27. 2022
    New Insights on Reducing Abrupt Representation Change in Online Continual LearningLucas Caccia, Rahaf Aljundi, Nader Asadi … Eugene BelilovskyICLR
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  28. 2019
    A Continual Learning Survey: Defying Forgetting in Classification TasksMatthias Delange, Rahaf Aljundi, Marc Masana … Tinne TuytelaarsTPAMI · Computer Vision Center · Huawei Technologies (Canada)
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  29. 2020
    Automatic Recall Machines: Internal Replay, Continual Learning and the BrainXu Ji, João F. Henriques, Tinne Tuytelaars, Andrea VedaldiarXiv
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  30. 2020
    Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open ProblemMatthias De Lange, Xu Jia, Sarah Parisot … Tinne TuytelaarsCVPR · KU Leuven · Huawei Technologies (Sweden)
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  31. 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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  32. 2020
    On the Exploration of Incremental Learning for Fine-grained Image RetrievalWei Chen, Yu Liu, Weiping Wang … Michael S. LewBMVC · Leiden University · KU Leuven · +1
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  33. 2019
    Online Continual Learning with Maximally Interfered RetrievalRahaf Aljundi, Lucas Caccia, Eugene Belilovsky … Tinne TuytelaarsarXiv · McGill University · Université de Montréal · +1
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  34. 2019
    Task-Free Continual LearningRahaf Aljundi, Klaas Kelchtermans, Tinne TuytelaarsCVPR · KU Leuven
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  35. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  36. 2018
    Memory Aware Synapses: Learning what (not) to forgetRahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny … Tinne TuytelaarsECCV · IMEC · KU Leuven · +2
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  37. 2017
    Encoder Based Lifelong LearningAmal Rannen, Rahaf Aljundi, Matthew B. Blaschko, Tinne TuytelaarsICCV · IMEC · KU Leuven
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  38. 2017
    Expert Gate: Lifelong Learning with a Network of ExpertsRahaf Aljundi, Punarjay Chakravarty, Tinne TuytelaarsCVPR · IMEC
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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.