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.

958 papers of 6,984 · showing 851–900Sort Recent · Most cited
  1. 2023
  2. 2023
    Learning Representations on the Unit Sphere: Application to Online Continual LearningNicolas Michel, Giovanni Chierchia, R. Negrel, Jean-François BercherarXiv
  3. 2023
    Meta Continual Learning on Graphs with Experience ReplayAltay Unal, A. Akgül, M. Kandemir, Gozde UnalTMLR
  4. 2023
    Adaptive Distribution Masked Autoencoders for Continual Test-Time AdaptationJiaming Liu, Ran Xu, Senqiao Yang … Shanghang ZhangarXiv
  5. 2023
  6. 2023
    From Categories to Classifier: Name-Only Continual Learning by Exploring the WebAmeya Prabhu, Hasan Abed Al Kader Hammoud, Ser-Nam Lim … Adel BibiarXiv
  7. 2023
    Active Continual Learning: Labelling Queries in a Sequence of TasksThuy-Trang Vu, Shahram Khadivi, Dinh Q. Phung, Gholamreza HaffariarXiv
  8. 2023
    Continually learning new languagesNgoc-Quan Pham, J. Niehues, A. WaibelarXiv
  9. 2023
  10. 2023
  11. 2023
    Optimizing Spca-based Continual Learning: A Theoretical ApproachChunchun Yang, Malik Tiomoko, Zengfu WangICLR
  12. 2023
    Self-Supervised Continual LearningK. Thakral, S. Mittal, Utkarsh Uppal … Richa SinghICLR
  13. 2023
    Severity of Catastrophic Forgetting in Object Detection for Autonomous DrivingC. Witte, René Schuster, S. Bukhari … Georg SchneiderICPR
  14. 2023
  15. 2023
  16. 2023
    CD-IMM: The Benefits of Domain-based Mixture Models in Bayesian Continual LearningDaniele Castellana, Antonio Carta, D. BacciuCLAI Unconf
  17. 2023
  18. 2023
    Deep Continual Learning (Dagstuhl Seminar 23122)T. Tuytelaars, Bing Liu, Vincenzo Lomonaco … Andrea CossuDagstuhl Reports
  19. 2023
    Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation DriftXueying Bai, Jinghuan Shang, Yifan Sun, Niranjan BalasubramanianPreprint
  20. 2023
  21. 2023
    Hessian Aware Low-Rank Weight Perturbation for Continual LearningJiaqi Li, Rui Wang, Yuanhao Lai … Fan ZhouarXiv
  22. 2023
    Issues for Continual Learning in the Presence of Dataset BiasDonggyu Lee, Sangwon Jung, Taesup MoonAAAI
  23. 2023
  24. 2023
    Primal-Dual Continual Learning: Stability and Plasticity through Lagrange MultipliersJuan Elenter, Navid Naderializadeh, Tara Javidi, Alejandro RibeiroarXiv
  25. 2023
  26. 2023
  27. 2023
  28. 2023
  29. 2023
  30. 2023
  31. 2023
  32. 2023
  33. 2023
  34. 2023
  35. 2023
  36. 2023
    E2Net: Resource-Efficient Continual Learning with Elastic Expansion NetworkRuiqi Liu, Boyu Diao, Libo Huang … Yong-Jun XuarXiv
  37. 2023
    Few-Shot Continual Learning for Conditional Generative Adversarial NetworksCat P. Le, Juncheng Dong, Ahmed Aloui, Vahid TarokharXiv
  38. 2023
    From IID to the Independent Mechanisms assumption in continual learningOleksiy Ostapenko, Pau Rodríguez López, Alexandre Lacoste, Laurent CharlinAAAI
  39. 2023
  40. 2023
    Joint Relation Modeling and Feature Learning for Class-Incremental Facial Expression RecognitionYuanling Lv, Yan Yan, Hanzi WangChinese Conference on Pattern Recognition and Computer Vi…
  41. 2023
    Label Delay in Continual LearningBotos Csaba, Wenxuan Zhang, Matthias Müller … Adel BibiarXiv
  42. 2023
  43. 2023
    Novel continual learning techniques on noisy label datasetsMonica Millunzi, Lorenzo Bonicelli, Alberto Zurli … Simone CalderaraItal-IA
  44. 2023
    Overcoming catastrophic forgetting with classifier expanderXinchen Liu, Hongbo Wang, Ying-Jian Tian, Linyao XieMachine Learning
  45. 2023
  46. 2023
    Supplementary Material for Dense Network Expansion for Class Incremental LearningZhiyuan Hu, Yunsheng Li, J. Lyu … N. VasconcelosPreprint
  47. 2023
  48. 2023
  49. 2023
    Treatment Effect Estimation to Guide Model Optimization in Continual LearningJonas Seng, Florian Peter Busch, M. Zecevic, Moritz WilligAAAI
  50. 2023
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.