Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 6,984Sort Recent · Most cited
  1. 2026
    GMPA: Enhancing Continual Learning with Rehearsal-Based Method via Gaussian Mixture Prototype AugmentationTrang Pham, Viet Xuan-Quang Trinh, Xuan-Hung Ho … Duc-Trong LeSpringer CCIS
  2. 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
  3. 2024
    FT-IBCL: Enhancing Imprecise Bayesian for Continual Learning Under Trade-Offs with Fine-TuningQuynh-Trang Pham Thi, Tu-Tai Hoang, Van-Toan Phan … Thanh Hai DangInternational Conference on Knowledge and System Engineer… · VNU University of Science · Hội Gastroenterology Việt Nam
  4. 2024
    Contrastive Learning for Boosting Knowledge Transfer in Task-Incremental Continual Learning of Aspect Sentiment Classification TasksThanh Hai Dang, Quynh-Trang Pham Thi, Duc-Trong Le, Tri-Thanh NguyenVNU Journal of Science Computer Science and Communication… · VNU University of Science · Universal Environmental Technologies (United States)
  5. 2024
    Towards Robust Continual Learning: A Multi-Head Approach with Online Prototype Equilibrium and Adaptive Prototypical FeedbackQuynh-Trang Pham Thi, Duc‐Hung Nguyen, Thanh Hai Dang … Quang-Thuy HaSpringer LNCS · VNU University of Science
  6. 2023
  7. 2019
    Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning ModelNgoc Vu Nguyen, Thi-Lan Nguyen, Cam-Van Thi Nguyen … Quang-Thuy HaVietnam Journal of Computer Science · Hanoi University of Natural Resources and Environment · Vietnam National University, Hanoi
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. 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.