Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 11,817Sort Recent · Most cited
  1. 2026
    Class-incremental feature selection based on neighborhood probabilistic rough setsXiao-Ling Yang, Yao Li, Hong-Mei Chen … Sha-Sha FengInformation Sciences
  2. 2026
    Structure-aware federated hypergraph continual learningYanxin Hu, Xiaoman Liu, Zhenzhen Xie … Chao ChengInformation Sciences
  3. 2026
    SA-LoRA: Shared-A decoupled low-rank adaptation for class-incremental learningXiaohuan Bing, Yuanhao Xi, Ramin YahyapourInformation Sciences
  4. 2026
  5. 2026
    Distribution-aware sampling of replay buffer for mitigating catastrophic forgettingArmaan Garg, Ansh Sharma, Aryan Arora, S. VermaInformation Sciences
  6. 2026
    TADG: topology-aware and distillation-guided framework for continual knowledge graph embeddingMing-Sheng Wang, Pengfei Wang, Ming He, Hong-Bin WangInformation Sciences
  7. 2026
    UniTrack: Unifying day and night tracking with continual learningJiwei Mo, Feixiang He, Pengzhi Zhong … Xianhao ShenInformation Sciences
  8. 2026
    LUNCH: adaptive balancing of continual learning via hyperparameter uncertaintyQingyi Pan, Li-Yuan Wang, Jingyi Zhang, Jun ZhuInformation Sciences
  9. 2026
    CRAD-HOPE: brain-inspired nested learning framework for few-shot anomaly detectionWen-Xin Cao, Juan-Hua Cao, Wei-Jun WuInformation Sciences
  10. 2026
    Online label aggregation with incomplete crowd responsesYuyang Liu, Haoyu Liu, Runze Wu … Changjie FanInformation Sciences
  11. 2026
    Class semantics guided knowledge distillation for few-shot class incremental learningPing Li, Jiajun Chen, Shaoqi Tian, Ran WangInformation Sciences
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.