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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    Retrieval-Augmented Pseudo-Image Guided Alignment and Text Domain-Aware Memory Recall for Continual Zero-Shot CaptioningBing Liu, Wenjie Yang, Mingming Liu … Yong ZhouIEEE TCSVT · China University of Mining and Technology · Jiangsu Vocational Institute of Architectural Technology · +1
  2. 2024
    Incremental Image Generation with Diffusion Models by Label Embedding Initialization and FusionBing Li, Dongdong Ren, Hao Liu … Yang Gaoon Continual Learning meets Multimodal Foundation Models:… · Nanjing University · Tencent (China)
  3. 2024
    L2A: Learning Affinity From Attention for Weakly Supervised Continual Semantic SegmentationHao Liu, Yong Zhou, Bing Liu … Joey Tianyi ZhouIEEE TCSVT · China University of Mining and Technology · Agency for Science, Technology and Research · +1
  4. 2023
    Distilled Meta-learning for Multi-Class Incremental LearningHao Liu, Zhaoyu Yan, Bing Liu … Abdulmotaleb El SaddikACM Transactions · China University of Mining and Technology · University of Ottawa
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  5. 2022
    Incremental learning with neural networks for computer vision: a surveyHao Liu, Yong Zhou, Bing Liu … Zhiwen ShaoArtificial Intelligence Review · Ministry of Education of the People's Republic of China · China University of Mining and Technology
  6. 2022PDF ↗
  7. 2020
    Fast Adapting Without Forgetting for Face RecognitionHao Liu, Xiangyu Zhu, Zhen Lei … Stan Z. LiIEEE TCSVT · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +3
  8. 2020
    An Incremental Learning Network Model Based on Random Sample Distribution FittingWencong Wang, Lan Huang, Hao Liu … Kangping WangSpringer LNCS · Jilin University · Jilin Province Science and Technology Department
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.