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. 2026
    Kernel-Prototype Guided Background Adaptation for Class-Incremental Semantic SegmentationViet-Anh Tran Ngoc, Dinh-Nhat Loi, Thanh-Hai Dang, Quynh-Trang Pham ThiSpringer LNCS · Industrial University of Ho Chi Minh City · Ho Chi Minh City University of Industry and Trade
  2. 2025
    ELLA: Enhancing Long-Tailed Online Continual Learning with Layer AugmentationDinh-Nhat Loi, Tri-Thanh Nguyen, Thanh-Hai Dang, Quynh-Trang Pham ThiInternational Conference on Knowledge and System Engineer…
  3. 2025
    ZeroRFF: A Random Fourier Features and Analytic Learning Method for Generalized Class Incremental LearningDuc‐Hung Nguyen, Tri-Thanh Nguyen, Thanh-Hai Dang, Quynh-Trang Pham ThiSpringer LNCS
  4. 2025
    Enhanced Class Incremental Semantic Segmentation with Initial Model Pre-tuningVan-Truong Le, Quynh-Trang Pham Thi, Tri-Thanh Nguyen, Thanh-Hai DangInternational Conference on Multimedia Analysis and Patte… · Ho Chi Minh City University of Science · University Of Information Technology · +1
  5. 2024
    Dynamic Prompt Selection for Resource-Efficient Continual Learning in Panoptic SegmentationQuynh-Trang Pham Thi, Minh-Anh Dang, Tri-Thanh Nguyen … Thanh-Hai DangRIVF International Conference on Computing and Communicat… · FPT University
  6. 2023
    Bert Adapter and Contrastive Learning for Continual Classification of Aspect Sentiment Task SequencesTrang Pham, Ngoc-Huyen Ngo, Dan-Truong Phan Dinh, Thanh-Hai DangVietnam Journal of Science and Technology/Science and Tec… · Hanoi National University of Education · Vietnam National University, Hanoi
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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.