Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

17 papers of 4,574Sort Recent · Most cited
  1. 2025
    Bayesian Low-Rank Factorization for Robust Model AdaptationE. Ugan, Ngoc-Quan Pham, Alexander H. WaibelICASSP
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  2. 2025PDF ↗
  3. 2025
    Towards Robust Visual Continual Learning with Multi-Prototype SupervisionXi-Wei Liu, Yu-Long Li, Yi-Chen Li … Imran RazzakICASSP
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  4. 2025
    Cross-Modal Knowledge Distillation for Speech Large Language ModelsEnzhi Wang, Qicheng Li, Zhiyuan Tang, Yuhang JiaICASSP
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  5. 2025
    Class-Incremental Learning for Sound Event Localization and DetectionRuchi Pandey, Manjunath Mulimani, Archontis Politis, Annamaria MesarosICASSP · Tampere University
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  6. 2025
    Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient TuningX.R. Li, Weijieying Ren, Wei Qin … Richang HongICASSP · Pennsylvania State University · Hefei University of Technology · +1
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  7. 2025
    HDMoLE: Mixture of LoRA Experts with Hierarchical Routing and Dynamic Thresholds for Fine-Tuning LLM-based ASR ModelsBingshen Mu, Kun Wei, Qijie Shao … Lei XieICASSP · Northwestern Polytechnical University · Tencent (China)
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  8. 2025
    Dynamic Object Queries for Transformer-based Incremental Object DetectionJichuan Zhang, Wei Li, Shuang Cheng … Shengjin WangICASSP · Tsinghua University · Chinese Academy of Sciences
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  9. 2025
    Freeze and Learn: Continual Learning with Selective Freezing for Speech Deepfake DetectionDavide Salvi, Viola Negroni, Luca Bondi … Stefano TubaroICASSP · Politecnico di Milano · Robert Bosch (United States)
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  10. 2025
    IOR: Inversed Objects Replay for Incremental Object DetectionZijia An, Boyu Diao, Libo Huang … Yongjun XuICASSP · Chinese Academy of Sciences · Institute of Computing Technology
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  11. 2025
    Continuously Learning New Words in Automatic Speech RecognitionChristian Huber, Alexander WaibelICASSP · Karlsruhe Institute of Technology
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  12. 2025
    Controllable Forgetting Mechanism for Few-Shot Class-Incremental LearningKirill Paramonov, Mete Özay, Eunju Yang … Umberto MichieliICASSP · Samsung (United Kingdom) · Samsung (South Korea)
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  13. 2025
    Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive FusionYukun Chen, Zihuan Qiu, Fanman Meng … Qingbo WuICASSP · University of Electronic Science and Technology of China
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  14. 2025
    Fresh-CL: Feature Realignment through Experts on Hypersphere in Continual LearningZhongyi Zhou, Yaxin Peng, Pengxing Yi … Chaomin ShenICASSP · East China Normal University · Shanghai University
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  15. 2025
    ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR PersonalizationHaaris Mehmood, Karthikeyan Saravanan, Pablo Peso Parada … Seokyeong JungICASSP · Samsung (United Kingdom) · Samsung (South Korea)
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  16. 2025PDF ↗
  17. 2025PDF ↗
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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.