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.

24 papers of 6,984Sort Recent · Most cited
  1. 2016
    A Fast, Robust, and Incremental Model for Learning High-Level Concepts From Human Motions by ImitationMina Alibeigi, Majid Nili Ahmadabadi, Babak Nadjar AraabiIEEE Transactions · University of Tehran · Institute for Research in Fundamental Sciences
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  2. 2016
    Incremental learning for bootstrapping object classifier modelsCem Karaoguz, Alexander GepperthIEEE Conference Proceedings · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +1
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  3. 2016
    A Growing Long-term Episodic & Semantic MemoryMarc Pickett, Rami Al‐Rfou, Louis Shao, Chris TararXiv
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  4. 2016
    Incremental One-Class Models for Data ClassificationTakoua Kefi, Riadh Ksantini, Mohamed Kaâniche, Adel BouhoulaarXiv
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  5. 2016
    A novel progressive multi-label classifier for class-incremental dataMihika Dave, Sahil Tapiawala, Meng Joo Er, Rajasekar VenkatesanIEEE International Conference on Systems, Man and Cyberne… · Birla Institute of Technology and Science, Pilani · Nanyang Technological University
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  6. 2016PDF ↗
  7. 2016
    Less-forgetting Learning in Deep Neural NetworksHeechul Jung, Jeongwoo Ju, Minju Jung, Junmo KimarXiv
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  8. 2016
    Progressive Neural NetworksAndrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins … Raia HadsellarXiv
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  9. 2016
    Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppressionStephen J. GottsPsychonomic Bulletin & Review · National Institutes of Health · National Institute of Mental Health
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  10. 2016
    Active Long Term Memory NetworksTommaso Furlanello, Jiaping Zhao, Andrew Saxe … Bosco S. TjanarXiv
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  11. 2016
    On the Effects of Spam Filtering and Incremental Learning for Web-Supervised Visual Concept ClassificationMatthias Springstein, Ralph Ewerth2016 ACM on International Conference on Multimedia Retrieval · Technische Informationsbibliothek (TIB) · Leibniz University Hannover
  12. 2016
    One-shot Learning with Memory-Augmented Neural NetworksAdam Santoro, Sergey Bartunov, Matthew Botvinick … Timothy LillicraparXiv
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  13. 2016
    Incremental Object Recognition in Robotics with Extension to New Classes in Constant TimeR. Camoriano, Giulia Pasquale, C. Ciliberto … G. MettaarXiv
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  14. 2016
    Online Open World RecognitionRocco De Rosa, Thomas Mensink, Barbara CaputoarXiv
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  15. 2016
    Semantic video labeling by developmental visual agentsMarco Gori, Marco Lippi, Marco Maggini, Stefano MelacciComputer Vision and Image Understanding · University of Siena · University of Bologna
  16. 2016
    Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web MiningYoung Jay, Valerio Basile, Kunze Lars … Nick HawesFrontiers · University of Birmingham · Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis
  17. 2016
    Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco, Davide MaltoniSpringer LNCS · University of Bologna
  18. 2016
    Analytical Incremental Learning: Fast Constructive Learning Method for Neural NetworkSyukron Abu Ishaq Alfarozi, Noor Akhmad Setiawan, Teguh Bharata Adji … Masanori SugimotoSpringer LNCS · Universitas Gadjah Mada · King Mongkut's Institute of Technology Ladkrabang · +1
  19. 2016
    Continual Learning through Evolvable Neural Turing MachinesBenno Lüders, Mikkel Schläger, S. RisiNeurIPS
  20. 2016
  21. 2016
  22. 2016
    Learning without ForgettingAuthors pendingECCV
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  23. 2016PDF ↗
  24. 2016
    Net2Net: Accelerating Learning via Knowledge TransferTianqi Chen, Ian Goodfellow, Jonathon ShlensICLR · University of Washington · Google (United States)
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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.