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.

532 papers of 6,984 · showing 451–500Sort Recent · Most cited
  1. 2021
    Principal Gradient Direction and Confidence Reservoir Sampling for Continual LearningZhiyi Chen, Tong LinSpringer LNCS · Georgia Institute of Technology · Peking University · +1
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  2. 2021
    Lifelong Learning from Event-based DataVadym Gryshchuk, Cornelius Weber, Chu Kiong Loo, Stefan WermterESANN 2021 proceedings · Universität Hamburg · University of Malaya
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  3. 2021
  4. 2021
    Designing deep neural networks for continual learning in an open worldMartin MundtGoethe-Universität Frankfurt am Main
  5. 2021
    Lifelong Explainer for Lifelong LearnersXuelin Situ, Sameen Maruf, Ingrid Zukerman … Gholamreza HaffariEMNLP · Monash University · Commonwealth Scientific and Industrial Research Organisation · +2
  6. 2021
    Class-Incremental Learning via Dual AugmentationFei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin LiuNeurIPS
  7. 2021
  8. 2021
  9. 2021
  10. 2021
    Long Live the Lottery: The Existence of Winning Tickets in Lifelong LearningTianlong Chen, Zhenyu (Allen) Zhang, Sijia Liu … Zhangyang WangICLR
  11. 2021
    Contextual Transformation Networks for Online Continual LearningQ. Pham, Chenghao Liu, Doyen Sahoo, S. HoiICLR
  12. 2021
    BNS: Building Network Structures Dynamically for Continual LearningQi Qin, Han Peng, Wen-Rui Hu … Bing LiuNeurIPS
  13. 2021
  14. 2021
    Continual Learning using a Bayesian Nonparametric Dictionary of Weight FactorsNikhil Mehta, Kevin J Liang, V. Verma, L. CarinAISTATS
  15. 2021
    Generative vs. Discriminative: Rethinking The Meta-Continual LearningMohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, Mahdieh SoleymaniNeurIPS
  16. 2021
  17. 2021
    Reducing Representation Drift in Online Continual LearningLucas Caccia, Rahaf Aljundi, T. Tuytelaars … Eugene BelilovskyarXiv
  18. 2021
    Rethinking the Representational Continuity: Towards Unsupervised Continual LearningDivyam Madaan, Jaehong Yoon, Yuanchun Li … Sung Ju HwangICLR
  19. 2021
  20. 2021
    Does Continual Learning = Catastrophic Forgetting?Anh Thai, S. Stojanov, Isaac Rehg, James M. RehgarXiv
  21. 2021
    Continual Learning of Semantic Segmentation using Complementary 2D-3D Data RepresentationsJonas Frey, H. Blum, Francesco Milano … César CadenaarXiv
  22. 2021
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  24. 2021
  25. 2021
    Match What Matters: Generative Implicit Feature Replay for Continual LearningKevin Thandiackal, Tiziano Portenier, Andrea Giovannini … Uppsala UniversityarXiv
  26. 2021
    Efficient Continual Adaptation for Generative Adversarial NetworksSakshi Varshney, V. Verma, L. Carin, Piyush RaiarXiv
  27. 2021
  28. 2021
  29. 2021
    Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual LearningK. Deja, Paweł Wawrzyński, Daniel Marczak … Tomasz Trzci'nskiarXiv
  30. 2021
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  33. 2021
    Continual Auxiliary Task LearningAuthors pendingNeurIPS
  34. 2021
  35. 2021
    Continual Learning with Memory CascadesD. Kappel, F. Negri, Christian TetzlaffPreprint
  36. 2021
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  40. 2021PDF ↗
  41. 2021
  42. 2021
    Lifelong Robot LearningE. Oztop, Emre UgurPreprint
  43. 2021
    MAML-CL: Edited Model-Agnostic Meta-Learning for Continual LearningMarcin Andrychowicz, Misha Denil, Sergio Gómez … Longxiang GaoPreprint
  44. 2021
  45. 2021
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  47. 2021
  48. 2021
    Supplementary Material: Rectification-based Knowledge Retention for Continual LearningPravendra Singh, Pratik Mazumder, Piyush Rai, Vinay P. NamboodiriPreprint
  49. 2021
    Supplementary Materials for "Generative vs Discriminative: Rethinking The Meta-Continual Learning"Mohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, M. BaghshahPreprint
  50. 2021
    Supplementary: Essentials for Class Incremental LearningSudhanshu Mittal, Silvio Galesso, T. BroxPreprint
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.