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. 2026
    Continual Test-Time Adaptation via Low-Frequency ModulationBoyuan Zhang, Jie Pan, Shuai Yang, Lichuan GuIEEE Access · Anhui Agricultural University · Anhui University
  2. 2026
    Deep Generative Replay-Based Personalization With Conditional Latent Attention for Diffusion ModelsHaruka Matsuda, Ren Togo, Keisuke Maeda … Miki HaseyamaIEEE Access · Hokkaido University
  3. 2026
    Same Methods, Different Rankings: Trainable Depth as an Evaluation Variable in Continual LearningPaul-Tiberiu Iordache, Elena Burceanu, Mihai DascăluIEEE Access · University of Science and Technology · Universitatea Națională de Știință și Tehnologie Politehnica București
  4. 2026
    Semi-Supervised 3-D Point Cloud Semantic Segmentation Using Multiscale Graph Attention and Class-Incremental LearningH. Ye, Wei Guo, Liang Ma … Jiahao MaIEEE Access · Qingdao Huanghai University · Namseoul University · +4
  5. 2026
    TILES: Transformer-Based Incremental Learning for Expanding SegmenterHejer Ammar, Saad Lahlali, Romaric AudigierIEEE Access · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies
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.