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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026
    Dream2Learn: Structured Generative Dreaming for Continual LearningSalvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri … Giovanni BellittoIJCV · 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 ZhanIJCV · Nanjing University
  3. 2026
    Sample-Aware Knowledge Association and Enhancement for Open-Vocabulary Continual LearningZhilin Zhu, Zhiheng Ma, Yabin Wang … Xiaopeng HongIJCV · Harbin Institute of Technology · Peng Cheng Laboratory · +2
  4. 2026
    Sparse Orthogonal Parameters Tuning for Continual LearningKun-Peng Ning, Hai-Jian Ke, Yuyang Liu … Yuan LiIJCV · Peking University Shenzhen Hospital · Peng Cheng Laboratory
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  5. 2026
    EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental LearningHuaijie Wang, De Cheng, Lingfeng He … Xinbo GaoIJCV · Xidian University · Northwestern Polytechnical University
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  6. 2026
    ReBac: Current-to-Past Residual Background Correction for Class-Incremental Semantic SegmentationGuangyu Gao, Anqi Zhang, Jianbo Jiao … Yunchao WeiIJCV · Beijing Institute of Technology · University of Birmingham · +1
  7. 2026
    Vision-Language Efficient Tuning for Mitigating Catastrophic Forgetting in Multi-Modal LearningYaoming Wang, Yuchen Liu, Wenrui Dai … Hongkai XiongIJCV · Shanghai Jiao Tong University · Huawei Technologies (China)
  8. 2026PDF ↗
  9. 2026
    COBRA: A Continual Learning Approach to Vision-Brain UnderstandingXuan-Bac Nguyen, Manuel Serna-Aguilera, Arabinda K. Choudhary … Ky LuuIJCV · University of Arkansas at Fayetteville · SUNY Upstate Medical University · +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.