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.

169 papers of 8,653 · showing 51–100Sort Recent · Most cited
  1. 2018
    Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data StreamsMahardhika Pratama, Andri Ashfahani, Yew-Soon Ong … Edwin LughoferarXiv
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  2. 2018
    StackNet: Stacking Parameters for Continual learningJangho Kim, Jeesoo Kim, Nojun KwakPreprint
  3. 2018
  4. 2018PDF ↗
  5. 2018
    Developmental Reinforcement Learning through Sensorimotor Space EnlargementMatthieu Zimmer, Yann Boniface, Alain DutechJoint IEEE 8th International Conference on Development an… · Laboratoire Lorrain de Recherche en Informatique et ses Applications · Inspire · +3
  6. 2018
    Life-Long Disentangled Representation Learning with Cross-Domain Latent HomologiesAlessandro Achille, Tom Eccles, Löıc Matthey … Irina HigginsNeurIPS
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  7. 2018PDF ↗
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  9. 2018
    A visual auditory model based on Growing Self-Organizing Maps to analyze the taxonomic response in early childhoodValentina Gliozzi, Matteo MadedduCognitive Systems Research · University of Turin
  10. 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
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  11. 2018
    Evolving Spiking Neural Networks for online learning over drifting data streamsJesús L. Lobo, Ibai Laña, Javier Del Ser … Nikola KasabovNeural Networks · Euskadiko Parke Teknologikoa · University of the Basque Country · +2
  12. 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
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  13. 2018
    Local learning rules to attenuate forgetting in neural networksMichael Deistler, Martino Sorbaro, Michael E. Rule, Matthias H. HennigarXiv
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  14. 2018
    On catastrophic forgetting and mode collapse in Generative Adversarial NetworksHoang Thanh-Tung, Truyen TranarXiv · Deakin University
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  15. 2018
  16. 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)
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  17. 2018
    Modeling Consecutive Task Learning with Task Graph AgendasDavid Isele, Eric Eaton, Mark Roberts, David W. AhaAdaptive Agents and Multi-Agents Systems · University of Pennsylvania · United States Naval Research Laboratory
  18. 2018PDF ↗
  19. 2018
    Embodiment can combat catastrophic forgettingJoshua Powers, Sam Kriegman, Josh BongardGenetic and Evolutionary Computation Conference Companion · University of Vermont
  20. 2018
    Policy and Value Transfer in Lifelong Reinforcement LearningDavid Abel, Yuu Jinnai, Yue (Sophie) Guo … M. LittmanICML
  21. 2018
    Combating catastrophic forgetting with developmental compressionShawn L. E. Beaulieu, Sam Kriegman, Josh C. BongardGenetic and Evolutionary Computation Conference · University of Vermont
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  22. 2018
    Enhancing CNN Incremental Learning Capability with an Expanded NetworkShanshan Cai, Zhuwei Xu, Zhichao Huang … C.‐C. Jay KuoIEEE International Conference on Multimedia and Expo (ICME) · University of Southern California · Tsinghua University
  23. 2018
    Adaptive Incremental Gaussian Mixture Network for Non-Stationary Data Stream ClassificationJorge C. Chamby-Diaz, Mariana Recamonde‐Mendoza, Ana L. C. Bazzan, Ricardo GrunitzkiIJCNN · Universidade Federal do Rio Grande do Sul
  24. 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
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  25. 2018
    Analysis of inner structure of VSF-NetworkYoshitsugu Kakemoto, Shinichi NakasukaIJCNN · The University of Tokyo
  26. 2018
    Lifelong Metric LearningGan Sun, Yang Cong, Ji Liu … Haibin YuIEEE Trans. Cybernetics · Shenyang Institute of Automation · University of Chinese Academy of Sciences · +3
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  27. 2018
    Evaluating and Characterizing Incremental Learning from Non-Stationary DataAlejandro Cervantes, Christian Gagné, Pedro Isasi, Marc ParizeauarXiv
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  28. 2018
    Meta Continual LearningRisto Vuorio, Dong-Yeon Cho, Daejoong Kim, Jiwon KimarXiv
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  29. 2018
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  31. 2018
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  33. 2018
    PackNet: Adding Multiple Tasks to a Single Network by Iterative PruningArun Mallya, Svetlana LazebnikCVPR · University of Illinois Urbana-Champaign
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  34. 2018
    New Metrics and Experimental Paradigms for Continual LearningTyler L. Hayes, Ronald Kemker, Nathan D. Cahill, Christopher KananCVPR · Rochester Institute of Technology
  35. 2018
    Subset Replay Based Continual Learning for Scalable Improvement of Autonomous SystemsPratik Prabhanjan Brahma, Adrienne OthonCVPR · Volkswagen Group (United States)
  36. 2018
    Adaptive Matrix Sketching and Clustering for Semisupervised Incremental LearningZilin Zhang, Yan Li, Zhengwen Zhang … Meiguo GaoIEEE Signal Processing Letters · Beijing Institute of Technology
  37. 2018
    Learn to Detect Objects IncrementallyLinting Guan, Yan Wu, Junqiao Zhao, Chen YeIEEE Intelligent Vehicles Symposium (IV) · Tongji University
  38. 2018
    Deep Face Detector Adaptation Without Negative Transfer or Catastrophic ForgettingMuhammad Abdullah Jamal, Haoxiang Li, Boqing GongCVPR · University of Central Florida · Adobe Systems (United States) · +1
  39. 2018
    Incremental Learning in Deep Convolutional Neural Network VIA Adaptive RegularizationSihyeon Seong, Pyunghwan Ahn, Jiwhan Kim, Junmo KimIEEE International Conference on Consumer Electronics - A… · Korea Advanced Institute of Science and Technology
  40. 2018
    Reinforced Continual LearningJu Xu, Zhanxing ZhuNeurIPS
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  41. 2018
    Distributed Weight Consolidation: A Brain Segmentation Case StudyPatrick McClure, Charles Zheng, Jakub Kaczmarzyk … Francisco PereiraNeurIPS · National Institutes of Health · Massachusetts Institute of Technology
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  42. 2018
  43. 2018
    Towards Robust Evaluations of Continual LearningSebastian Farquhar, Yarin GalICML · University of Oxford
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  44. 2018
    Online Structured Laplace Approximations For Overcoming Catastrophic ForgettingHippolyt Ritter, Aleksandar Botev, David BarberNeurIPS
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  45. 2018PDF ↗
  46. 2018
    Progress & Compress : A scalable framework for continual learningJonathan Schwarz, Jelena Luketina, Wojciech Marian Czarnecki … Raia HadsellICML
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  47. 2018
    INNAMP: An incremental neural network architecture with monitor perceptronSharad Gupta, Sudip SanyalAI Communications · Indian Institute of Information Technology Allahabad · BML Munjal University
  48. 2018
    Pseudorehearsal in Actor-Critic Agents with Neural Network Function ApproximationVladimir Marochko, Leonard Johard, Manuel Mazzara, Luca LongoInternational Conference on Advanced Information Networki… · Innopolis University
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  49. 2018
    Measuring Catastrophic Forgetting in Neural NetworksRonald Kemker, Marc McClure, Angelina Abitino … Christopher KananAAAI · Rochester Institute of Technology · Swarthmore College
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  50. 2018
    Selective Experience Replay for Lifelong LearningDavid Isele, Akansel CosgunAAAI · Honda (United States) · University of Pennsylvania · +1
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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. 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.