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. 2023
    Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer LearningLapo Frati, Neil Traft, Jeff Clune, Nick CheneyEuropean Conference on Artificial Intelligence
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  2. 2023
    Create and Find Flatness: Building Flat Training Spaces in Advance for Continual LearningWenhang Shi, Yiren Chen, Zhe Zhao … Xiaoyong DuEuropean Conference on Artificial Intelligence
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  3. 2023
    Offline Experience Replay for Continual Offline Reinforcement LearningSibo Gai, Donglin Wang, Li HeEuropean Conference on Artificial Intelligence
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  4. 2023
    Evolving Dictionary Representation for Few-shot Class-incremental LearningXuejun Han, Yuhong GuoEuropean Conference on Artificial Intelligence
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  5. 2023
    Using Self-Supervised Dual Constraint Contrastive Learning for Cross-Modal RetrievalXintong Wang, Xiaoyu Li, Liang Ding … Christian BiemannEuropean Conference on Artificial Intelligence
  6. 2023
    Ada-QPacknet - Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive PruningMarcin Pietroń, Dominik Żurek, Kamil Faber, Roberto CorizzoEuropean Conference on Artificial Intelligence
  7. 2023
    Data-Free Class-Incremental Learning with Implicit Representation of PrototypesTianwen Yang, Leixiong Huang, Ronghua LuoEuropean Conference on Artificial Intelligence
  8. 2023
    Adaptive Self-Supervised Continual LearningLilei Wu, Zhen Wang, Jie LiuEuropean Conference on Artificial Intelligence
  9. 2023
    R-STAR: Robust Self-Taught Task-Wise Reweighting for Rehearsal-Based Class Incremental LearningYutian Luo, Yizhao Gao, Haoran Wu … Zhiwu LuEuropean Conference on Artificial Intelligence
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.