Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

10 papers of 11,817Sort Recent · Most cited
  1. 2026
    Crafting Your Evolving Dreams: Concept-Incremental Versatile CustomizationJia-Hua Dong, Wen-Qi Liang, Hong-Liu Li … F. KhanTPAMI
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  2. 2025
    Bring Your Dreams to Life: Continual Text-to-Video CustomizationJiahua Dong, Xu-Dong Wang, Wen-Qi Liang … F. KhanAAAI
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  3. 2025PDF ↗
  4. 2025
    LLM Post-Training: A Deep Dive into Reasoning Large Language ModelsKomal Kumar, Tajamul Ashraf, Omkar Thawakar … F. KhanTPAMI
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  5. 2024
    How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?Jiahua Dong, Wen-Qi Liang, Hong-Liu Li … F. KhanNeurIPS
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  6. 2024
    Semi-supervised Open-World Object DetectionSahal Shaji Mullappilly, Abhishek Singh Gehlot, R. Anwer … Hisham CholakkalAAAI
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  7. 2024
    Continual Learning and Unknown Object Discovery in 3D Scenes via Self-distillationMohamed El Amine Boudjoghra, Jean Lahoud, Hisham Cholakkal … F. KhanECCV
  8. 2023
    Invariant convolutional neural network for robust and generalizable QoT estimation in fiber-optic networksQihang Wang, Zhuojun Cai, A. Lau … F. KhanJournal of Optical Communications and Networking
  9. 2019
    Random Path Selection for Continual LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
  10. 2019
    Random Path Selection for Incremental LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
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.