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.

10 papers of 8,653Sort Recent · Most cited
  1. 2022
    Sample Condensation in Online Continual LearningMattia Sangermano, Antonio Carta, Andrea Cossu, Davide BacciuIJCNN · University of Pisa · Scuola Normale Superiore
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  2. 2022
    Continual Learning for Human State MonitoringFederico Matteoni, Andrea Cossu, Claudio Gallicchio … Davide BacciuThe European Symposium on Artificial Neural Networks
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  3. 2022PDF ↗
  4. 2022
    Ex-Model: Continual Learning from a Stream of Trained ModelsAntonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide BacciuCVPR · University of Pisa · Scuola Normale Superiore
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  5. 2022
    Is Class-Incremental Enough for Continual Learning?Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini … Vincenzo LomonacoFrontiers · University of Pisa · Scuola Normale Superiore · +1
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  6. 2022
    Catastrophic Forgetting in Deep Graph Networks: A Graph Classification BenchmarkAntonio Carta, Andrea Cossu, Federico Errica, Davide BacciuFrontiers · University of Pisa · Scuola Normale Superiore
  7. 2022
    Distilled Replay: Overcoming Forgetting through Synthetic SamplesAndrea Rosasco, Antonio Carta, Andrea Cossu … Davide BacciuSpringer LNCS · University of Pisa · Scuola Normale Superiore
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  8. 2022
    Practical Recommendations for Replay-based Continual Learning MethodsGabriele Merlin, Vincenzo Lomonaco, Andrea Cossu … Davide BacciuSpringer LNCS · University of Pisa · Scuola Normale Superiore
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  9. 2022
    Avalanche RL: a Continual Reinforcement Learning LibraryNicoló Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide BacciuSpringer LNCS · University of Pisa
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  10. 2022
    Continual Incremental Language Learning for Neural Machine TranslationMichele Resta, Davide BacciuESANN 2022 proceedings · University of Pisa · Istituto Nazionale di Fisica Nucleare, Sezione di Pisa
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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.