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.

24 papers of 8,653Sort Recent · Most cited
  1. 2023
    Subspace Distillation for Continual LearningKaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash HarandiNeural Networks
    PDF ↗
  2. 2023
    Dual memory model for experience-once task-incremental lifelong learningGehua Ma, Runhao Jiang, Lang Wang, Huajin TangNeural Networks · Zhejiang University of Science and Technology · Zhejiang University · +1
  3. 2023
    Mitigate forgetting in few-shot class-incremental learning using different image viewsPratik Mazumder, Pravendra SinghNeural Networks · Indian Institute of Technology Jodhpur · Indian Institute of Technology Roorkee
  4. 2023
    VLAD: Task-agnostic VAE-based lifelong anomaly detectionKamil Faber, Roberto Corizzo, Bartłomiej Śnieżyński, Nathalie JapkowiczNeural Networks · Institute of Computer Science · AGH University of Krakow · +1
  5. 2023
    Continual Learning with Invertible Generative ModelsJary Pomponi, Simone Scardapane, Aurelio UnciniNeural Networks · Sapienza University of Rome
    PDF ↗
  6. 2023
    Multi-granularity knowledge distillation and prototype consistency regularization for class-incremental learningYanyan Shi, Dianxi Shi, Dianxi Shi … Chunping QiuNeural Networks · National University of Defense Technology · National Defense University
  7. 2023
    Lifelong learning on evolving graphs under the constraints of imbalanced classes and new classesLukas Galke, Iacopo Vagliano, Benedikt Franke … Ansgar ScherpNeural Networks · Max Planck Institute for Psycholinguistics · Amsterdam University Medical Centers · +3
    PDF ↗
  8. 2023
    Growing dendrites enhance a neuron's computational power and memory capacityWilliam B. Levy, Robert A. BaxterNeural Networks · University of Virginia · Baxter (United States)
    PDF ↗
  9. 2023
    Imitating the oracle: Towards calibrated model for class incremental learningFei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng‐Lin LiuNeural Networks · Chinese Academy of Sciences · Shandong Institute of Automation · +3
  10. 2023
    Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligenceYang Yang, Jie Huang, Dexiu HuNeural Networks · PLA Information Engineering University
  11. 2023
    Online continual learning with declarative memoryZhe Xiao, Zhekai Du, Ruijin Wang … Jingjing LiNeural Networks · China Electronics Technology Group Corporation · Hebei Science and Technology Department · +1
  12. 2023
    Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech RecognitionLeyuan Qu, Cornelius Weber, Stefan WermterNeural Networks · Universität Hamburg · Zhejiang Lab · +2
    PDF ↗
  13. 2023
    Improving transparency and representational generalizability through parallel continual learningM Paknezhad, Hamsawardhini Rengarajan, Chenghao Yuan … Hwee Kuan LeeNeural Networks · Agency for Science, Technology and Research · Bioinformatics Institute · +2
  14. 2023
    SuperFormer: Continual learning superposition method for text classificationMarko Zeman, Jana Faganeli Pucer, Igor Kononenko, Zoran BosnićNeural Networks · University of Ljubljana
    PDF ↗
  15. 2023
    Knowledge-Preserving continual person re-identification using Graph Attention NetworkZhaoshuo Liu, Chaolu Feng, Shuaizheng Chen, Jun HuNeural Networks
  16. 2023
    Leveraging joint incremental learning objective with data ensemble for class incremental learningPratik Mazumder, Mohammed Asad Karim, Indu Joshi, Pravendra SinghNeural Networks · Indian Institute of Technology Jodhpur · Carnegie Mellon University · +2
  17. 2023
    A Domain-Agnostic Approach for Characterization of Lifelong Learning SystemsMegan M. Baker, Alexander New, Mario Aguilar-Simon … Gautam K. VallabhaNeural Networks · Johns Hopkins University Applied Physics Laboratory · Teledyne Technologies (United States) · +13
    PDF ↗
  18. 2023
    Dynamic sparse coding-based value estimation network for deep reinforcement learningHaoli Zhao, Zhenni Li, Wensheng Su, Shengli XieNeural Networks · Guangdong University of Technology
  19. 2023
    Continual learning with attentive recurrent neural networks for temporal data classificationShao-Yu Yin, Yu Huang, Tien-Yu Chang … Vincent S. TsengNeural Networks · National Yang Ming Chiao Tung University · Industrial Technology Research Institute · +1
  20. 2023
    Efficient Perturbation Inference and Expandable Network for continual learningFei Du, Yun Yang, Ziyuan Zhao, Zeng ZengNeural Networks · Yunnan University · Agency for Science, Technology and Research · +1
  21. 2023
    CLAD: A realistic Continual Learning benchmark for Autonomous DrivingEli Verwimp, Kuo Yang, Sarah Parisot … Tinne TuytelaarsNeural Networks
    PDF ↗
  22. 2023
    Continual Object Detection: A review of definitions, strategies, and challengesAngelo Garangau Menezes, Gustavo de Moura, Cézanne Alves, André C. P. L. F. de CarvalhoNeural Networks
    PDF ↗
  23. 2023
    Generative Negative Replay for Continual LearningGabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo LomonacoNeural Networks
    PDF ↗
  24. 2023
    A biologically inspired architecture with switching units can learn to generalize across backgroundsDoris Voina, Eric Shea‐Brown, Ştefan MihalaşNeural Networks · University of Washington · University of Washington Applied Physics Laboratory · +2
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.