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.

79 papers of 8,653 · showing 51–79Sort Recent · Most cited
  1. 2023
    Leveraging joint incremental learning objective with data ensemble for class incremental learningPratik Mazumder, Mohammed Asad Karim, Indu Joshi, Pravendra SinghNeural Networks · Indian Institute of Technology Jodhpur · Carnegie Mellon University · +2
  2. 2020
    A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningMartin Mundt, Yongwon Hong, Iuliia Pliushch, Visvanathan RameshNeural Networks · Goethe University Frankfurt · Technische Universität Darmstadt · +1
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  3. 2023
    A Domain-Agnostic Approach for Characterization of Lifelong Learning SystemsMegan M. Baker, Alexander New, Mario Aguilar-Simon … Gautam K. VallabhaNeural Networks · Johns Hopkins University Applied Physics Laboratory · Teledyne Technologies (United States) · +13
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  4. 2023
    Efficient Perturbation Inference and Expandable Network for continual learningFei Du, Yun Yang, Ziyuan Zhao, Zeng ZengNeural Networks · Yunnan University · Agency for Science, Technology and Research · +1
  5. 2023
    CLAD: A realistic Continual Learning benchmark for Autonomous DrivingEli Verwimp, Kuo Yang, Sarah Parisot … Tinne TuytelaarsNeural Networks
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  6. 2022
    TaskDrop: A Competitive Baseline for Continual Learning of Sentiment ClassificationJian-Ping Mei, Yilun Zhen, Qianwei Zhou, Rui YanNeural Networks · Zhejiang University of Science and Technology · Zhejiang University of Technology
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  7. 2022
    Continuous learning of spiking networks trained with local rulesDmitry Antonov, Kirill Sviatov, Sergey SukhovNeural Networks · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · Ulyanovsk State Technical University
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  8. 2022
    Return of the normal distribution: Flexible deep continual learning with variational auto-encodersYongwon Hong, Martin Mundt, Sungho Park … Hyeran ByunNeural Networks · Yonsei University · Technische Universität Darmstadt · +1
  9. 2023
    Continual Object Detection: A review of definitions, strategies, and challengesAngelo Garangau Menezes, Gustavo de Moura, Cézanne Alves, André C. P. L. F. de CarvalhoNeural Networks
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  10. 2024
    Continual Pre-Training Mitigates Forgetting in Language and VisionAndrea Cossu, Tinne Tuytelaars, Antonio Carta … Davide BacciuNeural Networks
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  11. 2023
    Generative Negative Replay for Continual LearningGabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo LomonacoNeural Networks
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  12. 2022
    Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization NetworksKrishnakumar Santhakumar, Hamidreza KasaeiNeural Networks · University of Groningen
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  13. 2022
    Deep Bayesian Unsupervised Lifelong LearningTingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer DyNeural Networks · Bryant University · Bryan College · +1
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  14. 2023
    A biologically inspired architecture with switching units can learn to generalize across backgroundsDoris Voina, Eric Shea‐Brown, Ştefan MihalaşNeural Networks · University of Washington · University of Washington Applied Physics Laboratory · +2
  15. 2021
    Schematic Memory Persistence and Transience for Efficient and Robust Continual LearningYuyang Gao, Giorgio A. Ascoli, Liang ZhaoNeural Networks · Emory University · George Mason University · +2
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  16. 2021
    Continual Learning for Recurrent Neural Networks: an Empirical EvaluationAndrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide BacciuNeural Networks · University of Pisa · Scuola Normale Superiore · +1
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  17. 2022
    Using top-down modulation to optimally balance shared versus separated task representationsPieter Verbeke, Tom VergutsNeural Networks · Ghent University Hospital
  18. 2020
    Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learningFangzhou Xiong, Zhiyong Liu, Kaizhu Huang … Amir HussainNeural Networks · Shandong Institute of Automation · Institute of Automation · +7
  19. 2020
    Progressive learning: A deep learning framework for continual learningHaytham M. Fayek, Lawrence Cavedon, Hong Ren WuNeural Networks · RMIT University
  20. 2019
    Uncertainty-based modulation for lifelong learningAndrew Brna, Ryan C. Brown, Patrick Connolly … Mario Aguilar-SimonNeural Networks · Triangle · Teledyne Technologies (United States)
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  21. 2020
    Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learningDeboleena Roy, Priyadarshini Panda, Kaushik RoyNeural Networks · Purdue University West Lafayette
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  22. 2019
    Continuous Learning in Single-Incremental-Task ScenariosDavide Maltoni, Vincenzo LomonacoNeural Networks · University of Bologna
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  23. 2018
    Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural NetworksAndrea Soltoggio, Kenneth O. Stanley, Sebastian RisiNeural Networks · Loughborough University · University of Central Florida · +1
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  24. 2018
    Evolving Spiking Neural Networks for online learning over drifting data streamsJesús L. Lobo, Ibai Laña, Javier Del Ser … Nikola KasabovNeural Networks · Euskadiko Parke Teknologikoa · University of the Basque Country · +2
  25. 2019
    Continual Lifelong Learning with Neural Networks: A ReviewG. I. Parisi, Ronald Kemker, Jose L. Part … Stefan WermterNeural Networks
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  26. 2017
    Lifelong learning of human actions with deep neural network self-organizationGerman I. Parisi, Jun Tani, Cornelius Weber, Stefan WermterNeural Networks · Universität Hamburg · Hamburg University of Technology · +1
  27. 1998
    Distributed ARTMAP: a neural network for fast distributed supervised learningGail A. Carpenter, Boriana L. Milenova, Benjamin W. NoeskeNeural Networks · Boston University · Adaptive Cognitive Systems
  28. 1997
  29. 1996
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.