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.

277 papers of 6,984 · showing 51–100Sort Recent · Most cited
  1. 2019
    Compacting, Picking and Growing for Unforgetting Continual LearningSteven C. Y. Hung, Cheng-Hao Tu, Cheng‐En Wu … Chu-Song ChenNeurIPS
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  2. 2019
    Uncertainty-based modulation for lifelong learningAndrew Brna, Ryan C. Brown, Patrick Connolly … Mario Aguilar-SimonNeural Networks · Triangle · Teledyne Technologies (United States)
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  3. 2019
    Learning to Remember from a Multi-Task TeacherYuwen Xiong, Mengye Ren, Raquel UrtasunarXiv
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  4. 2019PDF ↗
  5. 2019
    Is Fast Adaptation All You Need?Khurram Javed, Hengshuai Yao, Martha WhitearXiv · University of Alberta · Huawei Technologies (China)
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  6. 2019
    IL2M: Class Incremental Learning With Dual MemoryEden Belouadah, Adrian PopescuICCV · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies
  7. 2019
    Incremental Learning Techniques for Semantic SegmentationUmberto Michieli, Pietro ZanuttighICCV · University of Padua
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  8. 2019
    Overcoming Catastrophic Forgetting With Unlabeled Data in the WildKibok Lee, Kimin Lee, Jinwoo Shin, Honglak LeeICCV · Korea Advanced Institute of Science and Technology · University of Michigan · +1
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  9. 2019
    AMP: Adaptive Masked Proxies for Few-Shot SegmentationMennatullah Siam, Boris N. Oreshkin, Martin JägersandICCV · University of Alberta
  10. 2019
    Lifelong GAN: Continual Learning for Conditional Image GenerationMengyao Zhai, Lei Chen, Fred Tung … Greg MoriICCV · Simon Fraser University · Borealis (Austria)
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  11. 2019
    Incremental Learning Using Conditional Adversarial NetworksYe Xiang, Ying Fu, Pan Ji, Hua HuangICCV · Beijing Institute of Technology · NEC (United States)
  12. 2019
    ACE: Adapting to Changing Environments for Semantic SegmentationZuxuan Wu, Xin Wang, Joseph E. Gonzalez … Larry S. DavisICCV · Berkeley College · University of California, Berkeley · +1
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  13. 2019
    Continual Learning by Asymmetric Loss Approximation With Single-Side OverestimationDong-Min Park, Seokil Hong, Bohyung Han, Kyoung Mu LeeICCV · Seoul National University · Samsung (South Korea)
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  14. 2019
    Continual Learning of New Sound Classes Using Generative ReplayZhepei Wang, Cem Subakan, Efthymios Tzinis … Laurent CharlinIEEE Workshop on Applications of Signal Processing to Aud… · University of Illinois Urbana-Champaign · Mila - Quebec Artificial Intelligence Institute
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  15. 2019
    Collaborative Learning Through Shared Collective Knowledge and Local ExpertiseJavad Mohammadi, Soheil KolouriIEEE 29th International Workshop on Machine Learning for… · Carnegie Mellon University · HRL Laboratories (United States)
  16. 2019
    Meta Module Generation for Fast Few-Shot Incremental LearningShudong Xie, Yiqun Li, Dongyun Lin … Sheng DongICCV · Agency for Science, Technology and Research · Institute for Infocomm Research
  17. 2019
    Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning ModelNgoc Vu Nguyen, Thi-Lan Nguyen, Cam-Van Thi Nguyen … Quang-Thuy HaVietnam Journal of Computer Science · Hanoi University of Natural Resources and Environment · Vietnam National University, Hanoi
  18. 2019
    Sequential Learning for Cross-Modal RetrievalGe Song, Xiaoyang TanICCV · Novelis (Canada) · Ministry of Industry and Information Technology · +1
  19. 2019
    From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning — Insights from Biological Systems on Adaptive FlexibilityMalte Schilling, Helge Ritter, Frank W. OhlIEEE International Conference on Systems, Man and Cyberne… · Bielefeld University · Otto-von-Guericke-Universität Magdeburg · +1
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  20. 2019
    HiFI: A Hierarchical Framework for Incremental Learning using Deep Feature RepresentationAnkita Raj, Anima Majumder, Swagat KumarIEEE International Conference on Robot and Human Interact… · Indian Council of Agricultural Research · Indian Institute of Technology Delhi
  21. 2019PDF ↗
  22. 2019
    Learning with Long-term Remembering: Following the Lead of Mixed Stochastic GradientYunhui Guo, Mingrui Liu, Tianbao Yang, Tajana RosingarXiv · University of California San Diego · University of Iowa
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  23. 2019
    Attacking Lifelong Learning Models with Gradient ReversionYunhui Guo, Mingrui Liu, Yandong Li … Tajana RosingPreprint
  24. 2019
  25. 2019
  26. 2019
  27. 2019
    Overcoming Catastrophic Forgetting via Hessian-free Curvature EstimatesLeonid Butyrev, G. Kontes, Christoffer Loeffler, Christopher MutschlerPreprint
  28. 2019
  29. 2019
    Differentiable Hebbian Consolidation for Continual LearningVithursan Thangarasa, Thomas Miconi, Graham W. TaylorPreprint
  30. 2019
    HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNINGS. Mohinta, R. P. Costa, Stéphane CiocchiPreprint
  31. 2019
    Minimizing Change in Classifier Likelihood to Mitigate Catastrophic ForgettingAshish Gaurav, Sachin Vernekar, Sean Sedwards … K. CzarneckiPreprint
  32. 2019
    Prototype Recalls for Continual LearningMengmi Zhang, Tao Wang, J. Lim, Jiashi FengPreprint
  33. 2019
    Task-agnostic Continual Learning via Growing Long-Term Memory NetworksGermán Kruszewski, Ionut-Teodor Sorodoc, Tomas MikolovPreprint
  34. 2019
    Toward an AI Physicist for Unsupervised LearningTailin Wu, Max TegmarkPhysical review. E · Theiss Research · Massachusetts Institute of Technology
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  35. 2019
    ContCap: A scalable framework for continual image captioningGiang Nguyen, Tae Joon Jun, Trung Tran … Daeyoung KimarXiv
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  36. 2019
    Continual learning: A comparative study on how to defy forgetting in classification tasksMatthias De Lange, Rahaf Aljundi, Marc Masana … T. TuytelaarsTPAMI
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  37. 2019
    Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong LearningXin Yao, Tianchi Huang, Chenglei Wu … Lifeng SunNeural Computation · Tsinghua University
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  38. 2019
    Neural Architecture Search for Class-incremental LearningShenyang Huang, Vincent François-Lavet, Guillaume RabusseauarXiv
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  39. 2019PDF ↗
  40. 2019
    Learning sparse representations in reinforcement learningJacob Rafati, David C. NoellearXiv · University of California, Merced
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  41. 2019
    Incremental Learning of Abnormalities in Autonomous SystemsHassan Zaal, Hafsa Iqbal, Damian Campo … Carlo S. RegazzoniIEEE International Conference on Advanced Video and Signa… · University of Genoa
  42. 2019
    Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity RecognitionJongwoo Lee, Ki-Sang HongIEICE Transactions on Fundamentals of Electronics Communi… · Pohang University of Science and Technology
  43. 2019
    Learning Continually from Low-shot Data StreamCanyu Le, Xihan Wei, Biao Wang … Chen, ZhongguiarXiv
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  44. 2019
    Collaborative Method for Incremental Learning on Classification and GenerationByungju Kim, Jae-Young Lee, Kyungsu Kim … Junmo KimICIP · Korea Advanced Institute of Science and Technology · Samsung (United States)
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  45. 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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  46. 2019
    Continual learning of context-dependent processing in neural networksGuanxiong Zeng, Yang Chen, Bo Cui, Shan YuNature Machine Intelligence · Chinese Academy of Sciences · Institute of Automation · +2
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  47. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  48. 2019
    Toward Understanding Catastrophic Forgetting in Continual LearningCuong V. Nguyen, Alessandro Achille, Michael Lam … Stefano SoattoarXiv
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  49. 2019PDF ↗
  50. 2019
    Biologically inspired sleep algorithm for artificial neural networksGiri P. Krishnan, Timothy Tadros, Ramyaa Ramyaa, Maxim BazhenovarXiv
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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. 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.