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.

10 papers of 6,984Sort Recent · Most cited
  1. 2019
    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
    PDF ↗
  2. 2019
    Revisiting Distillation and Incremental Classifier LearningKhurram Javed, Faisal ShafaitSpringer LNCS · National University of Sciences and Technology
    PDF ↗
  3. 2019
    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
    PDF ↗
  4. 2019
    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
    PDF ↗
  5. 2019
    A Study on Catastrophic Forgetting in Deep LSTM NetworksMonika Schak, Alexander GepperthSpringer LNCS · Fulda University of Applied Sciences
  6. 2019
    Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural NetworksAlexander Gepperth, Florian WiechSpringer LNCS · Fulda University of Applied Sciences
  7. 2019
    Overcoming Catastrophic Interference with Bayesian Learning and Stochastic Langevin DynamicsMikhail Leontev, Alexander Mikheev, Kirill Sviatov, Sergey SukhovSpringer LNCS · Ulyanovsk State University · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · +1
  8. 2019
    Lifelong Learning Starting From ZeroClaes Strannegård, Herman Carlström, Niklas Engsner … Morteza Haghir ChehreghaniSpringer LNCS · Chalmers University of Technology
    PDF ↗
  9. 2019
    Central-Diffused Instance Generation Method in Class Incremental LearningMing-Yu Liu, Yijie WangSpringer LNCS · National University of Defense Technology
  10. 2019
    Transfer Learning with Sparse Associative MemoriesQuentin Jodelet, Vincent Gripon, Masafumi HagiwaraSpringer LNCS · Keio University · IMT Atlantique
    PDF ↗
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.