Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

15 papers of 11,817Sort Recent · Most cited
  1. 2018
    DeeSIL: Deep-Shallow Incremental LearningEden Belouadah, Adrian PopescuSpringer LNCS · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies
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  2. 2018
    Revisiting Distillation and Incremental Classifier LearningKhurram Javed, Faisal ShafaitSpringer LNCS · National University of Sciences and Technology
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  3. 2018
    Adding New Tasks to a Single Network with Weight Trasformations using Binary MasksMassimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota BulòSpringer LNCS · Fondazione Bruno Kessler · Sapienza University of Rome · +2
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  4. 2018
    Marginal Replay vs Conditional Replay for Continual LearningTimothée Lesort, Alexander Gepperth, Andrei Stoian, David FilliatSpringer LNCS · École Nationale Supérieure de Techniques Avancées · Thales (France) · +1
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  5. 2018
    Distractor-aware Siamese Networks for Visual Object TrackingZheng Zhu, Qiang Wang, Bo Li … Weiming HuSpringer LNCS · Chinese Academy of Sciences · Shandong Institute of Automation · +2
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  6. 2018
    Riemannian Walk for Incremental Learning: Understanding Forgetting and IntransigenceArslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, Philip H. S. TorrSpringer LNCS · University of Oxford
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  7. 2018
    End-to-End Incremental LearningFrancisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil … Karteek AlahariSpringer LNCS · Universidad de Málaga · University of Córdoba · +5
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  8. 2018
    Lifelong Learning via Progressive Distillation and RetrospectionSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinSpringer LNCS · University of Science and Technology of China · Chinese University of Hong Kong · +1
  9. 2018
    A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and ProtocolsNeerav Karani, Krishna Chaitanya, Christian F. Baumgartner, Ender KonukoğluSpringer LNCS · ETH Zurich
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  10. 2018
    Learn the new, keep the old: Extending pretrained models with new anatomy and imagesFırat Özdemir, Philipp Fuernstahl, Orçun GökselSpringer LNCS · ETH Zurich · University of Zurich · +1
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  11. 2018
    Catastrophic Forgetting: Still a Problem for DNNsBenedikt Pfülb, Alexander Gepperth, Syahrul Afzal Che Abdullah, Axel KilianSpringer LNCS · Fulda University of Applied Sciences
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  12. 2018
    Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network AugmentationAbel Zacarias, Luı́s A. AlexandreSpringer LNCS · University of Beira Interior · Instituto de Telecomunicações
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  13. 2018
    A Broad Neural Network Structure for Class Incremental LearningWenzhang Liu, Haiqin Yang, Yuewen Sun, Changyin SunSpringer LNCS · Southeast University · Hang Seng University of Hong Kong
  14. 2018
    Overcoming Catastrophic Forgetting with Self-adaptive IdentifiersFangzhou Xiong, Zhiyong Liu, Xu YangSpringer LNCS · Shandong Institute of Automation · University of Chinese Academy of Sciences · +2
  15. 2018
    On Capacity with Incremental Learning by Simplified Chaotic Neural NetworkToshinori Deguchi, Naohiro IshiiSpringer LNCS · National Institute of Technology, Gifu College · Aichi Institute of Technology
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 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.