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.

3 papers of 6,984Sort 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
    DBAB: A Dual-Branch Adaptive Balance Framework with Optimized Plasticity Branch for Class-Incremental LearningXinyu Chen, Heqian Qiu, Chenghao Qi … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · University of Electronic Science and Technology of China
  3. 2025
    OrthCal: Synergizing Orthogonal Contrastive Learning and Prototype Calibration for Few-Shot Class-Incremental LearningRuisong Dai, Hanwen Zhang, Xinyu Chen … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · 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.