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.

11 papers of 8,653Sort Recent · Most cited
  1. 2026
    FedFST: Mitigating Spectral Catastrophic Forgetting in Federated Graph Continual LearningHanyao Guo, Zihan Tan, Wenke Huang … Mang YeKDD · Wuhan University · Nanyang Technological University
  2. 2025PDF ↗
  3. 2025
    Spatio-Temporal Prediction on Streaming Data: A Unified Federated Continuous Learning FrameworkHao Miao, Yan Zhao, Chenjuan Guo … Christian S. JensenTKDE · Aalborg University · East China Normal University
  4. 2024
    QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models - Extended VersionDavid Campos, Bin Yang, Tung Kieu … Christian S. JensenVLDB · Aalborg University · East China Normal University · +1
    PDF ↗
  5. 2024
    Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior CorrectionRobert A. Marsden, Mario Döbler, Bin YangWACV · University of Stuttgart
    PDF ↗
  6. 2022
    Dirichlet Prior Networks for Continual LearningFelix Wiewel, Alexander Bartler, Bin YangIJCNN · University of Stuttgart
  7. 2021
    Condensed Composite Memory Continual LearningFelix Wiewel, Bin YangIJCNN · University of Stuttgart
    PDF ↗
  8. 2020
    Entropy-based Sample Selection for Online Continual LearningFelix Wiewel, Bin YangEuropean Signal Processing Conference (EUSIPCO) · University of Stuttgart
  9. 2020
    Continual Learning Through One-Class Classification Using VAEFelix Wiewel, Andreas Brendle, Bin YangICASSP · University of Stuttgart
  10. 2019PDF ↗
  11. 2019
    Continual Learning for Anomaly Detection with Variational AutoencoderFelix Wiewel, Bin YangICASSP · University of Stuttgart
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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.