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.

7 papers of 8,653Sort Recent · Most cited
  1. 2025
    Learning Order Matters in Class-Incremental Learning for Sound Localization and DetectionRuchi Pandey, Manjunath Mulimani, Archontis Politis, Annamaria MesarosEuropean Signal Processing Conference (EUSIPCO) · Tampere University of Applied Sciences · Tampere University
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  2. 2024
    Forgetting Analysis by Module Probing for Online Object Detection with Faster R-CNNBaptiste Wagner, Denis Pellerin, Sylvain HuetEuropean Signal Processing Conference (EUSIPCO) · Université Grenoble Alpes
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  3. 2022
    Harnessing the Power of Explanations for Incremental Training: A LIME-Based ApproachArnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey … Tinoosh MohseninEuropean Signal Processing Conference (EUSIPCO) · Infineon Technologies (United States) · University of Maryland, Baltimore County
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  4. 2023
    Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System*Khazar Khorrami, María Andrea Cruz Blandón, Tuomas Virtanen, Okko RäsänenEuropean Signal Processing Conference (EUSIPCO) · Tampere University
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  5. 2021
    Continual Learning for Monolingual End-to-End Automatic Speech RecognitionSteven Vander Eeckt, Hugo Van hammeEuropean Signal Processing Conference (EUSIPCO) · KU Leuven
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  6. 2021
    Entropy-based Sample Selection for Online Continual LearningFelix Wiewel, Bin YangEuropean Signal Processing Conference (EUSIPCO) · University of Stuttgart
  7. 2021
    Learning without Forgetting for Decentralized Neural Nets with Low Communication OverheadXinyue Liang, Alireza M. Javid, Mikael Skoglund, Saikat ChatterjeeEuropean Signal Processing Conference (EUSIPCO) · KTH Royal Institute of Technology
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.