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.

5 papers of 8,653Sort Recent · Most cited
  1. 2025
    Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental LearningShiwon Kim, Dongjun Hwang, Sungwon Woo, Rita SinghICCV · Yonsei University · Sogang University · +1
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  2. 2025
    Playbook: Scalable Discrete Skill Discovery From Unstructured Datasets for Long-Horizon Decision-Making ProblemsMinjae Kang, Mineui Hong, Songhwai OhRA-L · Seoul National University · Carnegie Mellon University
  3. 2025
    FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene UnderstandingThanh-Dat Truong, Utsav Prabhu, Bhiksha Raj … Khoa LuuCVPR · Google (United States) · Carnegie Mellon University · +2
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  4. 2025
    Dynamic Expansion Diffusion Learning for Lifelong Generative ModellingFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +1
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  5. 2025
    Continual Unsupervised Generative Modelling via Online Optimal TransportFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +2
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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.