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.

63 papers of 8,653 · showing 51–63Sort Recent · Most cited
  1. 2023
    CafeBoost: Causal Feature Boost to Eliminate Task-Induced Bias for Class Incremental LearningBenliu Qiu, Hongliang Li, Haitao Wen … Lili PanCVPR · University of Electronic Science and Technology of China
  2. 2023
    Heterogeneous Continual LearningDivyam Madaan, Hongxu Yin, Wonmin Byeon … Pavlo MolchanovCVPR · New York University
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  3. 2023
    Multi-Centroid Task Descriptor for Dynamic Class Incremental InferenceTenghao Cai, Zhizhong Zhang, Xin Tan … Yuan XieCVPR · East China Normal University · Xiamen University · +1
  4. 2023
    CLVOS23: A Long Video Object Segmentation Dataset for Continual LearningAmir Nazemi, Zeyad Moustafa, Paul FieguthCVPR · University of Waterloo
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  5. 2023
    Continual Domain Adaptation through Pruning-aided Domain-specific Weight ModulationB Prasanna, Sunandini Sanyal, R. Venkatesh BabuCVPR · Indian Institute of Science Bangalore
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  6. 2023
    Lifelong Learning of Task-Parameter Relationships for Knowledge TransferShikhar Srivastava, Mohammad Yaqub, Karthik NandakumarCVPR · Mohamed bin Zayed University of Artificial Intelligence
  7. 2023
    Density Map Distillation for Incremental Object CountingChenshen Wu, Joost van de WeijerCVPR · Barcelona Supercomputing Center · Computer Vision Center
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  8. 2023
    Batch Model Consolidation: A Multi-Task Model Consolidation FrameworkIordanis Fostiropoulos, Jiaye Zhu, Laurent IttiCVPR · University of Southern California
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  9. 2023
    Simulating Task-Free Continual Learning Streams From Existing DatasetsAristotelis Chrysakis, Marie‐Francine MoensCVPR · KU Leuven
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  10. 2023
    Online Distillation with Continual Learning for Cyclic Domain ShiftsJoachim Houyon, A. Cioppa, Yasir Ghunaim … Marc Van DroogenbroeckCVPR
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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.