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.

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Springer CCIS Clear all

2021
Metric Learning with Distillation for Overcoming Catastrophic ForgettingPiaoyao Yu, Juanjuan He, Qilang Min, Qi ZhuSpringer CCIS · Wuhan University of Science and Technology
2021
Regular Decision Processes for Grid WorldsNicky Lenaers, Martijn van OtterloSpringer CCIS · Open University of the Netherlands · Radboud University Nijmegen
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2021
On robustness of generative representations against catastrophic forgettingWojciech Masarczyk, Kamil Rafał Deja, T. P. TrzcinskiSpringer CCIS · Warsaw University of Technology · Jagiellonian University
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2021
Self-supervised Continual Learning for Object Recognition in Image SequencesRuiqi Dai, Mathieu Lefort, Frédéric Armetta … Stefan DuffnerSpringer CCIS · Lyon 1 Université · Centre National de la Recherche Scientifique · +3
2021
Sparse Progressive Neural Networks for Continual LearningEsra Ergün, Behçet Uğur TöreyınSpringer CCIS · Istanbul Technical University
2020
NASIL: Neural Network Architecture Searching for Incremental Learning in Image ClassificationXianya Fu, Wenrui Li, Qiurui Chen … Rui WangSpringer CCIS · Beihang University · State Key Joint Laboratory of Environment Simulation and Pollution Control
2020
Average Jane, Where Art Thou? – Recent Avenues in Efficient Machine Learning Under Subjectivity UncertaintyGeorgios Rizos, Björn W. SchullerSpringer CCIS · Imperial College London · University of Augsburg · +1
2017
Pseudorehearsal Approach for Incremental Learning of Deep Convolutional Neural NetworksDiego Mellado, Carolina Saavedra, Stéren Chabert, Rodrigo SalasSpringer CCIS · University of Valparaíso
2016
On Learning Parameters of Incremental Learning in Chaotic Neural NetworkToshinori Deguchi, Naohiro IshiiSpringer CCIS · National Institute of Technology, Gifu College · Aichi Institute of Technology
2010
A Comparison between Growing and Variably Dense Self Organizing Maps for Incremental Learning in Hubel Weisel Models of Concept RepresentationNeo Choon kiat Daniel, Kiruthika Ramanathan, Luping Shi, Prahlad VadakkepatSpringer CCIS · Agency for Science, Technology and Research · Data Storage Institute · +1

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.