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.

5 papers of 8,653Sort Recent · Most cited
  1. 2024
    Adapt Without Forgetting: Distill Proximity from Dual Teachers in Vision-Language ModelsMengyu Zheng, Yehui Tang, Zhiwei Hao … Chang XuSpringer LNCS · The University of Sydney · Huawei Technologies (Canada) · +1
  2. 2021
    Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender SystemsKian Ahrabian, Yishi Xu, Yingxue Zhang … Mark CoatesACM International Conference on Information & Knowled… · McGill University · Huawei Technologies (Canada)
  3. 2021
    TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph CompletionJiapeng Wu, Yishi Xu, Yingxue Zhang … Jackie Chi Kit CheungSIGIR · McGill University · Université de Montréal · +1
    PDF ↗
  4. 2019
    A Continual Learning Survey: Defying Forgetting in Classification TasksMatthias Delange, Rahaf Aljundi, Marc Masana … Tinne TuytelaarsTPAMI · Computer Vision Center · Huawei Technologies (Canada)
    PDF ↗
  5. 2020
    GraphSAIL: Graph Structure Aware Incremental Learning for Recommender SystemsYishi Xu, Yingxue Zhang, Wei Guo … Mark CoatesCIKM · Université de Montréal · Huawei Technologies (Canada) · +2
    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 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.