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
    Dream2Learn: Structured Generative Dreaming for Continual LearningSalvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri … Giovanni BellittoInternational Journal of Computer Vision · University of Catania
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  2. 2026
    Bi-Compatible Task-Agnostic Feature Augmentation for Expansion-Based Class-Incremental LearningBowen Zheng, Zijun Shen, Da-Wei Zhou … De‐Chuan ZhanInternational Journal of Computer Vision · Nanjing University
  3. 2026
    Sample-Aware Knowledge Association and Enhancement for Open-Vocabulary Continual LearningZhilin Zhu, Zhiheng Ma, Yabin Wang … Xiaopeng HongInternational Journal of Computer Vision · Harbin Institute of Technology · Peng Cheng Laboratory · +2
  4. 2026
    ReBac: Current-to-Past Residual Background Correction for Class-Incremental Semantic SegmentationGuangyu Gao, Anqi Zhang, Jianbo Jiao … Yunchao WeiInternational Journal of Computer Vision · Beijing Institute of Technology · University of Birmingham · +1
  5. 2026
    Vision-Language Efficient Tuning for Mitigating Catastrophic Forgetting in Multi-Modal LearningYaoming Wang, Yuchen Liu, Wenrui Dai … Hongkai XiongInternational Journal of Computer Vision · Shanghai Jiao Tong University · Huawei Technologies (China)
  6. 2026
    Few-shot Class-Incremental Learning via Generative Co-Memory RegularizationKexin Bao, Yong Li, Dan Zeng, Shiming GeInternational Journal of Computer Vision
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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. 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.