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. 2025
    Do Your Best and Get Enough Rest for Continual LearningHankyul Kang, Gregor Seifer, Dong‐Hyun Lee, Jongbin RyuCVPR · Ajou University · Korea Advanced Institute of Science and Technology · +1
    PDF ↗
  2. 2025
    T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental LearningSeong-Hyeon Hwang, Minsu Kim, Steven Euijong WhangCVPR · Korea Advanced Institute of Science and Technology · Kootenay Association for Science & Technology
    PDF ↗
  3. 2024
    ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt TuningBeomyoung Kim, Joonsang Yu, Sung Ju HwangCVPR · NAVER Cloud (South Korea) · Naver (South Korea) · +2
    PDF ↗
  4. 2023
    EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled RegularizationJunha Song, Jungsoo Lee, In So Kweon, Sungha ChoiCVPR · Korea Advanced Institute of Science and Technology · Qualcomm (United Kingdom) · +1
    PDF ↗
  5. 2020
    Continual Learning With Extended Kronecker-Factored Approximate CurvatureJanghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, Junmo KimCVPR · Korea Advanced Institute of Science and Technology
    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. 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.