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.

6 papers of 8,653Sort Recent · Most cited
  1. 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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  2. 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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  3. 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
  4. 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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  5. 2022
    Deep Bayesian Unsupervised Lifelong LearningTingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer DyNeural Networks · Bryant University · Bryan College · +1
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  6. 2022
    Using top-down modulation to optimally balance shared versus separated task representationsPieter Verbeke, Tom VergutsNeural Networks · Ghent University Hospital
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.