Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

9 papers of 11,817Sort Recent · Most cited
  1. 2022
    CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual LearningJames Seale Smith, Leonid Karlinsky, Vyshnavi Gutta … Zsolt KiraCVPR · Georgia Institute of Technology · IBM (United States) · +1
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  2. 2022
    A Closer Look at Rehearsal-Free Continual Learning *James Seale Smith, Junjiao Tian, Shaunak Halbe … Zsolt KiraCVPR · Georgia Institute of Technology · Samsung (United States) · +1
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  3. 2022
    System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy GamesIndranil Sur, Zachary Daniels, Abrar Rahman … Aswin RaghavanInternational Conference on AI-ML-Systems · SRI International · American University · +3
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  4. 2022PDF ↗
  5. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
  6. 2022
    A Closer Look at Knowledge Distillation with Features, Logits, and GradientsYen-Chang Hsu, James Smith, Yilin Shen … Hongxia JinarXiv
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  7. 2021
    Always Be Dreaming: A New Approach for Data-Free Class-Incremental LearningJames Smith, Yen-Chang Hsu, Jonathan Balloch … Zsolt KiraICCV · Georgia Institute of Technology · Samsung (United States) · +1
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  8. 2021
    Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay BufferJames Smith, Jonathan Balloch, Yen-Chang Hsu, Zsolt KiraIJCNN · Georgia Institute of Technology · Samsung (United States)
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  9. 2019
    Leveraging Semantics for Incremental Learning in Multi-Relational EmbeddingsAngel Daruna, Weiyu Liu, Zsolt Kira, Sonia ChernovaarXiv · Georgia Institute of Technology
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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.