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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2021
Balanced Softmax Cross-Entropy for Incremental LearningQuentin Jodelet, Xin Liu, Tsuyoshi MurataComputer Vision and Image Understanding · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
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2020
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future DirectionsVincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodríguez … Davide MaltoniArtificial Intelligence · University of Bologna · Mila - Quebec Artificial Intelligence Institute · +7
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2020
SOINN+, a Self-Organizing Incremental Neural Network for Unsupervised Learning from Noisy Data StreamsChayut Wiwatcharakoses, Daniel BerrarExpert Systems with Applications · Tokyo Institute of Technology
2019
Group Wi-Lo: Maintaining Wi-Fi-based Indoor Localization Accurate via Group-wise Total Variation RegularizationMasato Sugasaki, Kota Tsubouchi, Masamichi Shimosaka, Nobuhiko NishioInternational Conference on Indoor Positioning and Indoor… · Tokyo Institute of Technology · Ritsumeikan University
2019
Self-Organizing Incremental Neural Networks for Continual LearningChayut Wiwatcharakoses, Daniel BerrarIJCAI · Tokyo Institute of Technology
2013
Incremental Learning Framework for Indoor Scene RecognitionAram Kawewong, Rapeeporn Pimup, Osamu HasegawaAAAI · Chiang Mai University · Tokyo Institute of Technology
2004
A self-structurizing neural network for online incremental learningOsamu Hasegawa, Furao ShenSociety of Instrument and Control Engineers of Japan · Tokyo Institute of Technology
1995
A functional analytic approach to incremental learning in optimally generalizing neural networksSethu Vijayakumar, Haruo OgawaICNN'95 - International Conference on Neural Networks · Tokyo 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.