Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 4,574Sort Recent · Most cited
  1. 2023PDF ↗
  2. 2023PDF ↗
  3. 2023
    Towards Adversarially Robust Continual LearningTao Bai, Chen Chen, Lingjuan Lyu … Bihan WenICASSP · Nanyang Technological University · Zhejiang University of Science and Technology · +2
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  4. 2023
    Continual Learning for On-Device Speech Recognition Using Disentangled ConformersAnuj Diwan, Ching-Feng Yeh, Wei-Ning Hsu … Abdelrahman MohamedICASSP · The University of Texas at Austin
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  5. 2023
    Centroid Distance Distillation for Effective Rehearsal in Continual LearningDaofeng Liu, Fan Lyu, Linyan Li … Fuyuan HuICASSP · Suzhou University of Science and Technology · Tianjin University · +1
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  6. 2023
    Federated Self-Learning with Weak Supervision for Speech RecognitionMilind Rao, Gopinath Chennupati, Gautam Tiwari … Jasha DroppoICASSP · Amazon (United States)
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  7. 2023
    Is Multi-Task Learning an Upper Bound for Continual Learning?Zihao Wu, Huy Tran, Hamed Pirsiavash, Soheil KolouriICASSP · Vanderbilt University · University of California, Davis
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  8. 2023
    Robustness-Preserving Lifelong Learning Via Dataset CondensationJinghan Jia, Yihua Zhang, Dogyoon Song … Alfred O. HeroICASSP · Michigan State University · University of Michigan
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  9. 2023
    Dynamic Scalable Self-Attention Ensemble for Task-Free Continual LearningFei Ye, Adrian G. BorşICASSP · University of York
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  10. 2023
    Biologically-Inspired Continual Learning of Human Motion SequencesJ. C. Ott, Shih‐Chii LiuICASSP · SIB Swiss Institute of Bioinformatics · University of Zurich · +1
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  11. 2023PDF ↗
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times, and only papers with a PDF we can point you at, so every title opens the paper itself. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.