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.

4 papers of 8,653Sort Recent · Most cited
  1. 2025
    D3Net: Dual-Path Decoupling-Distillation for Adaptive Fusion in Continual Egocentric LearningChenghao Qi, Heqian Qiu, Zhaofeng Shi … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · University of Electronic Science and Technology of China
  2. 2025
    Adaptively forget with crossmodal and textual distillation for class-incremental video captioningHuiyu Xiong, Lanxiao Wang, Heqian Qiu … Hongliang LiNeurocomputing · University of Electronic Science and Technology of China
  3. 2025
    Geodesic-Aligned Gradient Projection for Continual Task LearningBenliu Qiu, Heqian Qiu, Haitao Wen … Hongliang LiTIP · University of Electronic Science and Technology of China
  4. 2025
    Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding ConfusionShaoxu Cheng, Kanglei Geng, Chiyuan He … Hongliang LiIEEE Trans. Multimedia · University of Electronic Science and Technology of China
    PDF ↗
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.