Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 2024
    Class Incremental Learning with Multi-Teacher DistillationHaitao Wen, Lili Pan, Yu Dai … Hongliang LiCVPR · University of Electronic Science and Technology of China
  2. 2024
    Dual-Consistency Model Inversion for Non-Exemplar Class Incremental LearningZihuan Qiu, Yi Xu, Fanman Meng … Qingbo WuCVPR · University of Electronic Science and Technology of China · Dalian University of Technology
  3. 2024
    Continual Cross-Domain Image Compression via Entropy Prior Guided Knowledge Distillation and Scalable DecodingChenhao Wu, Qingbo Wu, Rui Ma … Heqian QiuIEEE TCSVT · University of Electronic Science and Technology of China
  4. 2024
    InfoUCL: Learning Informative Representations for Unsupervised Continual LearningLiang Zhang, Jiangwei Zhao, Qingbo Wu … Hongliang LiIEEE Trans. Multimedia · University of Electronic Science and Technology of China
  5. 2024
    Where to Forget: A New Attention Stability Metric for Continual Learning EvaluationHaojie Wang, Qingbo Wu, Hongliang Li, Fanman MengSpringer CCIS · University of Electronic Science and Technology of China
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.