Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4,574 papers · showing 4451–4500Sort Recent · Most cited
  1. 2018
    Life-Long Disentangled Representation Learning with Cross-Domain Latent HomologiesAlessandro Achille, Tom Eccles, Löıc Matthey … Irina HigginsNeurIPS
    PDF ↗
  2. 2018PDF ↗
  3. 2018
    Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural NetworksAndrea Soltoggio, Kenneth O. Stanley, Sebastian RisiNeural Networks · Loughborough University · University of Central Florida · +1
    PDF ↗
  4. 2018
    Rotate your Networks: Better Weight Consolidation and Less Catastrophic ForgettingXialei Liu, Marc Masana, Luis Herranz … Andrew D. BagdanovICPR · Computer Vision Center · University of Florence
    PDF ↗
  5. 2018
    Local learning rules to attenuate forgetting in neural networksMichael Deistler, Martino Sorbaro, Michael E. Rule, Matthias H. HennigarXiv
    PDF ↗
  6. 2018
    On catastrophic forgetting and mode collapse in Generative Adversarial NetworksHoang Thanh-Tung, Truyen TranarXiv · Deakin University
    PDF ↗
  7. 2018
    Multi-Agent Distributed Lifelong Learning for Collective Knowledge AcquisitionMohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric EatonAdaptive Agents and Multi-Agent Systems · University of Pennsylvania · HRL Laboratories (United States)
    PDF ↗
  8. 2018PDF ↗
  9. 2018
    Combating catastrophic forgetting with developmental compressionShawn L. E. Beaulieu, Sam Kriegman, Josh C. BongardGenetic and Evolutionary Computation Conference · University of Vermont
    PDF ↗
  10. 2018
    Fast Factorization-free Kernel Learning for Unlabeled Chunk Data StreamsYi Wang, Nan Xue, Xin Fan … Zhongxuan LuoIJCAI · Dalian University of Technology · University of Rochester
    PDF ↗
  11. 2018
    Lifelong Metric LearningGan Sun, Yang Cong, Ji Liu … Haibin YuIEEE Trans. Cybernetics · Shenyang Institute of Automation · University of Chinese Academy of Sciences · +3
    PDF ↗
  12. 2018
    Evaluating and Characterizing Incremental Learning from Non-Stationary DataAlejandro Cervantes, Christian Gagné, Pedro Isasi, Marc ParizeauarXiv
    PDF ↗
  13. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
    PDF ↗
  14. 2018
    Meta Continual LearningRisto Vuorio, Dong-Yeon Cho, Daejoong Kim, Jiwon KimarXiv
    PDF ↗
  15. 2018PDF ↗
  16. 2018
    PackNet: Adding Multiple Tasks to a Single Network by Iterative PruningArun Mallya, Svetlana LazebnikCVPR · University of Illinois Urbana-Champaign
    PDF ↗
  17. 2018
    Reinforced Continual LearningJu Xu, Zhanxing ZhuNeurIPS
    PDF ↗
  18. 2018
    Distributed Weight Consolidation: A Brain Segmentation Case StudyPatrick McClure, Charles Zheng, Jakub Kaczmarzyk … Francisco PereiraNeurIPS · National Institutes of Health · Massachusetts Institute of Technology
    PDF ↗
  19. 2018
    Towards Robust Evaluations of Continual LearningSebastian Farquhar, Yarin GalICML · University of Oxford
    PDF ↗
  20. 2019
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
    PDF ↗
  21. 2018
    Online Structured Laplace Approximations For Overcoming Catastrophic ForgettingHippolyt Ritter, Aleksandar Botev, David BarberNeurIPS
    PDF ↗
  22. 2018PDF ↗
  23. 2018
    Progress & Compress : A scalable framework for continual learningJonathan Schwarz, Jelena Luketina, Wojciech Marian Czarnecki … Raia HadsellICML
    PDF ↗
  24. 2018
    Measuring Catastrophic Forgetting in Neural NetworksRonald Kemker, Marc McClure, Angelina Abitino … Christopher KananAAAI · Rochester Institute of Technology · Swarthmore College
    PDF ↗
  25. 2018
    Selective Experience Replay for Lifelong LearningDavid Isele, Akansel CosgunAAAI · Honda (United States) · University of Pennsylvania · +1
    PDF ↗
  26. 2018
    Dynamic Few-Shot Visual Learning Without ForgettingSpyros Gidaris, Nikos KomodakisCVPR
    PDF ↗
  27. 2018
    Differentiable plasticity: training plastic neural networks with backpropagationThomas Miconi, Jeff Clune, Kenneth O. StanleyICML · Neurosciences Institute · University of Wyoming · +1
    PDF ↗
  28. 2018
    HOUDINI: Lifelong Learning as Program SynthesisLazar Valkov, Dipak Chaudhari, Akash Srivastava … Swarat ChaudhuriNeurIPS · Indian Institute of Technology Bombay · IBM (United States) · +2
    PDF ↗
  29. 2018
    Task Agnostic Continual Learning Using Online Variational BayesChen Zeno, Itay Golan, Elad Hoffer, Daniel SoudryNeurIPS
    PDF ↗
  30. 2018
    Memory-based Parameter AdaptationPablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae … Charles BlundellICLR · Google (United States)
    PDF ↗
  31. 2018
    One Big Net For EverythingJuergen SchmidhuberarXiv
    PDF ↗
  32. 2018
    Unicorn: Continual Learning with a Universal, Off-policy AgentDaniel J. Mankowitz, Augustin Žídek, André Barreto … Tom SchaularXiv
    PDF ↗
  33. 2019
    Continual Lifelong Learning with Neural Networks: A ReviewG. I. Parisi, Ronald Kemker, Jose L. Part … Stefan WermterNeural Networks
    PDF ↗
  34. 2018PDF ↗
  35. 2018
    Continual Reinforcement Learning with Complex SynapsesChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
    PDF ↗
  36. 2018
    Bayesian Incremental Learning for Deep Neural NetworksMax Kochurov, Timur Garipov, Dmitry Podoprikhin … Dmitry VetrovICLR · Skolkovo Institute of Science and Technology · Samsung (South Korea) · +1
    PDF ↗
  37. 2018
    Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural NetworksCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsarXiv
    PDF ↗
  38. 2018
    Incremental Classifier Learning with Generative Adversarial NetworksYue Wu, Yinpeng Chen, Lijuan Wang … Yun FuarXiv
    PDF ↗
  39. 2018PDF ↗
  40. 2018
    Focused learning promotes continual task performance in humansTimo Flesch, Jan Balaguer, Ronald Dekker … Christopher SummerfieldbioRxiv · University of Oxford · Science Oxford
    PDF ↗
  41. 2018
    Overcoming catastrophic forgetting with hard attention to the taskJoan Serrà, Dídac Surís, Marius Miron, Alexandros KaratzoglouICML
    PDF ↗
  42. 2018
    Memory Aware Synapses: Learning what (not) to forgetRahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny … Tinne TuytelaarsECCV · IMEC · KU Leuven · +2
    PDF ↗
  43. 2018
    Riemannian Walk for Incremental Learning: Understanding Forgetting and IntransigenceArslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, Philip H. S. TorrECCV · University of Oxford
    PDF ↗
  44. 2018
    End-to-End Incremental LearningFrancisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil … Karteek AlahariECCV · Universidad de Málaga · University of Córdoba · +5
    PDF ↗
  45. 2018PDF ↗
  46. 2018
    Catastrophic Forgetting: Still a Problem for DNNsBenedikt Pfülb, Alexander Gepperth, Syahrul Afzal Che Abdullah, Axel KilianSpringer LNCS · Fulda University of Applied Sciences
    PDF ↗
  47. 2018
    Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network AugmentationAbel Zacarias, Luı́s A. AlexandreSpringer LNCS · University of Beira Interior · Instituto de Telecomunicações
    PDF ↗
  48. 2018
    Continuous Learning in a Hierarchical Multiscale Neural NetworkThomas Wolf, Julien Chaumond, Clément DelangueACL · Central European University · Bio Signal Group (United States)
    PDF ↗
  49. 2018
    Alleviating Catastrophic Forgetting with Modularity for Continuously Learning Linked Open DataLu Chen, Masayuki MurataInternational Journal of Computer Theory and Engineering · The University of Osaka
    PDF ↗
  50. 2017PDF ↗
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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.